PREvencion con DIetaMEDiterranea (PREDIMED) 14-item Mediterranean Diet Adherence Screener (MEDAS), a brief questionnaire assessing adherence to the Mediterranean diet (MedDiet), which was used in the PREDIMED trial for assessment and immediate feedback. This instrument (MEDAS) was administered to 96 adults with a high cardiovascular risk , recruited from general practices in Bristol, UK. Participants then completed a 3-day estimated food record, and the MEDAS was administered again one month later. A MedDiet score (range = 0–14) was calculated from the MEDAS’ administrations and food record to assess concurrent validity and test-retest reliability. Predictive validity was assessed by examining the association of the MEDAS-derived score with cardiometabolic risk factors and dietary intakes derived from the food records. The MEDAS-derived MedDiet score was higher by 1.47 points compared to food records , correlated moderately with the record-derived score  and there was borderline fair agreement between the two methods . Exact agreement within score categories and gross misclassificationwere 45.8% and 21.9%, respectively. The distribution of dietary intakes, reported on the food records by the MEDAS-derived total MedDiet score, was in the expected direction, but no association was observed with cardiometabolic risk factors. The two administrations of the MEDAS produced similar mean total MedDiet scores , which were correlated  and agreed fairly . The English version of the MEDAS has acceptable accuracy and reliability for assessing MedDiet adherence among individuals with a high cardiovascular risk, in the UK, and can be used to rank individuals according to MedDiet adherence in research and practice.The aim of this study was to examine the validity of the English version of the  Cardiovascular disease (CVD) is an important global public health problem associated with death and disability . In the The Mediterranean diet (MedDiet), rich in olive oil, nuts, fruits and vegetables, whole grains and pulses, low-fat dairy, fish, moderate alcohol amounts and limited quantities of red meat and sweets, has been the factor most frequently cited to explain the low CVD incidence and mortality  as well r=0.52, p < 0.001; κ statistic = 0.43), when compared to an extensive, full-length FFQ [p < 0.001) to high-density lipoprotein (HDL) cholesterol and inversely (p < 0.038) to body mass index, waist circumference, triglycerides (TG), the TG/HDL ratio, fasting glucose, and the total cholesterol/HDL ratio. The 10-year estimated coronary artery disease risk decreased as the 14-item PREDIMED score increased (p < 0.001) [To enable the assessment of the MedDiet in the UK and to measure change towards this dietary pattern in response to an intervention, it is important to identify an accurate and reliable instrument to assess MedDiet adherence. Dietary assessment via food frequency questionnaires (FFQs), food records and dietary recalls underpins the evidence base for the role of the MedDiet in health promotion, but is impractical for dietary counselling and provision of advice in nutrition interventions, since it increases burden for participants and practitioners alike . In contngth FFQ . The ave< 0.001)  and 16 h< 0.001) . In cont< 0.001) . In addiDuring January 2015, all 34 general practices in the city of Bristol, UK, were invited by the Clinical Research Network (West of England) to facilitate the study, and ten expressed willingness. We recruited six general practices, purposively sampled to ensure variation in socioeconomic/ethnicity profiles of their populations, to undertake database searches, according to participant inclusion/exclusion criteria, and send an invitation letter to eligible patients (a detailed patient information sheet was enclosed with the invitation).2, being institutionalized, lacking autonomy, unable to attend visits, participants whose high CVD risk had not been communicated to them by their doctor or inadequate understanding of verbal/written information given in English [Similar to the PREDIMED study , eligibl English . ParticiData was collected between May and August 2015 at participants’ general practice premises by the same trained researcher, to minimize the potential for inter-interviewer reliability bias. Standard operating procedures were followed to ensure standardization of data collection among participants. The first appointment involved the collection of a non-fasting blood sample and anthropometric and blood pressure measurements, and the completion of an interviewer-administered demographic questionnaire and the interviewer-administered MEDAS. Participants were then provided with detailed verbal and written instructions to complete a 3-day estimated food record, which they were instructed to start completing approximately two weeks following their first appointment. A second appointment was arranged, approximately one month following the participant’s first appointment, where completed food records were returned and the MEDAS was administered again via an interview.A non-fasting venous blood sample was collected in appropriate vacutainers by a researcher trained in venipuncture and sent to the Clinical Biochemistry Department of the Bristol Royal Infirmary on the day of collection, for total cholesterol, HDL-cholesterol, TG, non-HDL-cholesterol and glycosylated hemoglobin (HbA1c) analysis, using calibrated equipment. Low-density lipoprotein (LDL) cholesterol values were calculated using the Friedewald equation . The rat2). Waist circumference was measured to the nearest millimeter, using a tape at a vertical plane, midway between the lower rib and iliac crest, and with the participant standing and gently breathing out. This measurement was obtained twice, and the mean was calculated.Body weight was measured using a calibrated digital scale , to the nearest 0.1 kg, with the participant barefoot and in light clothing. Body height was measured using a wall-mounted calibrated stadiometer , to the nearest millimeter, with the participant barefoot. Body mass index (BMI) was calculated as weight divided by height, squared (kg/mSystolic (SBP) and diastolic (DBP) blood pressure measurements were obtained from the left arm with an electronic sphygmomanometer, with the participant sitting and after having rested quietly for five minutes. Blood pressure was obtained three times, with an interval of two minutes between measurements. The mean of the second and third measurements was calculated and presented in the current report.Participants were asked to report their sex, date of birth, ethnicity , maritalThe MEDAS is a 14-item questionnaire requesting participants to report the habitual frequency of consumption or amount consumed of 12 main components of the MedDiet and two food habits related to the MedDiet ,14. EachA 3-day estimated food record  was usedUpon return, the food record was reviewed on site by the researcher for completeness with regards to portion sizes, cooking methods and description of foods, and any uncertainties were resolved with participants. Food records were coded by the same researcher, in order to minimize coding variability, and checked for accuracy by a second member of the research team (blinded with respect to the MEDAS score), using the Dietplan6 nutrition analysis software . Dietplan6 is based on the sixth edition of McCance and Widdowson’s Composition of Foods , updatedn and percentages) were used to report demographic and clinical characteristics of participants.We estimated that a sample size of 100 participants would have 80% power to detect a correlation in the total MedDiet score between the MEDAS and the 3-day food record of at least 0.3 . This would give 95% confidence intervals (CIs) for limits of agreement of approximately ±0.34 standard deviations . All anar correlation coefficient and intra-class correlation coefficients  were used to assess the relative agreement between the two dietary assessment methods. The mean MedDiet score, derived from the MEDAS and the food record, was categorized into the same three equal groups . Participants’ cross-classification and the percentage of participants classified into the same, adjacent and opposite categories were determined and agreement within categories between the two dietary assessment methods was then estimated using the kappa (κ) statistic. In validation studies of dietary assessment instruments, κ values from 0 to 0.20 indicate poor agreement, 0.21 to 0.40 fair agreement, 0.41 to 0.60 moderate agreement, 0.61 to 0.80 good agreement and 0.81 to 1 very good agreement [κ statistic, to understand if concordance in the total MedDiet score was driven by individual score components.The mean total MedDiet score was calculated from the MEDAS and the 3-day estimated food record. Pearson’s greement . Bland agreement ,28. AgreLinear regression models were fitted to examine the association between the total MEDAS-derived MedDiet score with cardiometabolic, anthropometric and blood pressure variables. General ordinary least-squares linear modeling was used to estimate energy-adjusted food and nutrient intakes, derived from the food records, according to tertile distribution of the MEDAS-derived MedDiet score (entered as a continuous variable in the model). The linear trend was assessed by polynomial contrasts and a post hoc Bonferroni correction was used for multiple comparisons.r correlation coefficient and ICC (two-way mixed effects model with average measures) were used to assess relative agreement between the two repeated MEDAS administrations. The mean MedDiet score was categorized into three equal groups, separately for each MEDAS administration .The mean total MedDiet score was calculated from the first and second administrations of the MEDAS. Pearson’s κ) statistic and Bland and Altman plots with 95% limits of agreement.Participant cross-classification and the percentage of them classified into the same, adjacent and opposite categories were established, and agreement within categories between the first and second MEDAS administration was then estimated using the kappa (2). Ninety-nine patients at high CVD risk were recruited from six general practices in Bristol . The proportion of participants classified into the same category of the total MedDiet score by the MEDAS and the food records was 45.8%, whereas gross misclassification (classification into opposite categories) was 21.9%. The κ statistic was 0.19 , indicating borderline fair agreement between the two dietary assessment methods [Ninety-six participants completed the MEDAS at their first appointment and returned a food record. Compared to food records, the MEDAS estimated a higher mean total MedDiet score by 1.47 points . There was strong evidence of a moderate correlation between the MEDAS-derived MedDiet score and the food record MedDiet score ( methods .p < 0.001), red meat , wine , nuts  and sofrito . Concordance was highest for wine , whereas total olive oil consumed, and pulses had no agreement. Overall, poor, fair, moderate and good concordance was found for 57.1, 28.6, 7.1 and 7.1% of the components of the MedDiet score, respectively.The Bland–Altman plot for the total MedDiet score from the two dietary assessment methods is illustrated in Linear regression analyses found no evidence against the null hypothesis of no associations between the total MedDiet score, derived by the MEDAS, and cardiometabolic, and anthropometric and blood pressure variables .The distribution of food and nutrient intakes calculated from the food records by tertiles of the MEDAS-derived MedDiet score were generally in the expected direction, with strong levels of evidence observed for olive oil, vegetable, red meat and sugar-sweetened beverage consumption as well as intake of carbohydrates, total fat, monounsaturated and polyunsaturated fat and vitamin E .p = 0.706) and relative agreement was good , indicating good reliability [κ was 0.38 , indicating fair agreement between the two MEDAS administrations [Ninety-six participants completed the MEDAS twice for reliability testing. The two administrations of the MEDAS produced a similar mean total MedDiet score  [r = 0.72, p = 0.002; ICC = 0.64, 95% CI 0.31–0.85) [κ = 0.43) demonstrated between the MEDAS and an FFQ in Spain [The moderate relative agreement between the MEDAS- and food record-derived MedDiet scores observed in our study is comparable to the first study assessing the accuracy of the MEDAS among Spanish older adults at high risk of CVD (Pearson’s < 0.001) . This su31–0.85) . Furtherin Spain . We acknin Spain  and Germin Spain  validatiin Spain . Classifin Spain , which min Spain .It should be noted, however, that comparisons of earlier studies with the current study should be undertaken with caution, due to the different reference instruments utilized to assess the accuracy of the MEDAS. For example, the reference dietary assessment method in all earlier MEDAS validation studies was a full-length validated FFQ, assessing dietary habits over the previous 12 months ,15,16. ADifferences in the intakes of the MEDAS food components between the studies might also account for the observed differences in absolute agreement. For example, only 1% of participants in our sample met the criterion for olive oil intake by both the MEDAS and food records, compared to 70.9% and 63.9% of Spanish adults meeting this criterion by the MEDAS and FFQ, respectively . These hIn the current study, participants who were in the highest tertile of MedDiet adherence, as indicated by the MEDAS-derived MedDiet score, had higher intakes of olive oil, vegetables, total, monounsaturated and polyunsaturated fat and vitamin E, and lower intakes of red/processed meat and sugar-sweetened beverages, compared to participants in the lowest MedDiet adherence tertile. These findings are in agreement with the original validation study of the MEDAS in older Spanish adults . The eviLimitations of the study include the self-selection of older adults at high risk of CVD as participants, who might already have made favourable changes to their diet, due to their knowledge of their risk. It is noteworthy, however, that participants had low adherence to the MedDiet, as indicated by both the MEDAS- and food record-derived MedDiet scores, which suggests that further dietary changes could be made to improve health. Our findings might not be generalisable to younger, healthy adults, and further assessment among a more ethnically and socio-economically diverse sample would establish the English MEDAS’ wider utility potential. There is no reason to believe that dietary reporting would vary in other populations; however, further validation studies would confirm this. Finally, the design of our study did not allow the assessment of the MEDAS’ ability to detect changes in MedDiet adherence and therefore, its potential utility in the evaluation of nutrition interventions aiming to promote the MedDiet. Nevertheless, the PREDIMED study demonstrated that the MEDAS can detect meaningful increases in MedDiet adherence following intervention , so therDespite the aforementioned limitations, in this sample of adults at high risk of CVD in a non-Mediterranean country, the English version of the MEDAS provided an acceptable estimate of adherence to the MedDiet against the reference instrument of estimated food records, as well as reasonable rankings according to the derived MedDiet score. Further, the MEDAS had good test-retest reliability, indicating that it is a reliable instrument for assessing MedDiet adherence over time. Considering the need to identify valid dietary assessment instruments that reduce burden for research participants and researchers, as well as patients and practitioners , the Eng


Familial Parkinson's disease (PD) is often caused by mutation of a certain gene, while sporadic PD is associated with variants of genes which can influence the susceptibility to PD. The goal of this study was to investigate the difference between the two forms of PD in terms of brain abnormalities using resting-state functional MRI and graph theory. Thirty-one familial PD patients and 36 sporadic PD patients underwent resting-state functional MRI scanning. Frequency-dependent functional connectivity was calculated for each subject using wavelet-based correlations of BOLD signal over 246 brain regions from Brainnetome Atlas. Graph theoretical analysis was then performed to analyze the topology of the functional network, and functional connectome differences were identified with a network-based statistical approach. Our results revealed a frequency-specific (0.016 and 0.031 Hz) connectome difference between familial and sporadic forms of PD, as indicated by an increase in assortativity and decrease in the nodal strength in the left medial amygdala of the familial PD group. In addition, the familial PD patients also showed a distinctive functional network between the left medial amygdala and regions related to retrieval of motion information. The present study indicates that the medial amygdala might be most vulnerable to both sporadic and familial PD. Our findings provide some new insights into disrupted resting-state functional connectomes between sporadic PD and familial PD. Parkinson's disease (PD) is a neurodegenerative disease with symptoms such as resting tremor, rigidity, akinesia and postural instability as shown by Parkinson . It was Resting functional Magnetic Resonance Imaging (R-fMRI) measures the intrinsic or spontaneous neuronal activity of the brain  with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the ethics committee of Central South University .The PD patients were diagnosed based on their history and a neurologic examination conducted by a fellowship-trained movement disorders specialist. The clinical diagnosis of PD was made based on the UK Parkinson's Disease Society Brain Bank (UKPDBB) criteria , and other general exclusion criteria for MRI scanning .All patients were studied in the off-medication state after all antiparkinsonian drugs had been withheld. Before the patients were scanned, they underwent a series of neurological examinations and neuropsychological testing including assessments with the Unified Parkinson's Disease Rating Scale (UPDRS)  in the Department of Radiology of Xiangya Hospital of Central South University. Earplugs were used to reduce scanner noise, and head motion was restricted via a foam pillow and extendable padded head clamps. The participants were instructed to rest with their eyes closed, relax and lie still. Functional scans were acquired using a gradient echo EPI (GE-EPI) sequence with the following parameters: www.fil.ion.ucl.ac.uk/spm); and DPARSF . The first 10 volumes were discarded to achieve scanner equilibrium. The remaining 170 images underwent slice timing and motion correction, and no subjects were found with head motion more than 1.5 mm of translation or greater than 1.5° of rotation in any of the x, y, or z axis. Next, the images were spatially normalized to Montreal Neurological Institute (MNI) space with the voxel size of 3 × 3 × 3 mm. No spatial smoothing was performed to avoid degradation of diagnosticity  that included 210 cortical and 36 subcortical subregions according to the Brainnetome atlas  = 1.659 × 10−6. The entry aij was the Pearson's correlation coefficient if the Bonferroni adjusted p-value was below the statistical threshold  and was set to 0 otherwise, as shown in Equation (1).Wavelet-based frequency analysis has been brought up to further the understanding of the pathophysiologies of various diseases  was defined as the average of the clustering coefficients over all nodes reflecting the possibility that the neighbors of a vertex would also be connected. Cp is between the range of 0 and 1, and is equal to 1 if and only if the network is fully connected. The characteristic path length Lp is the average of the length of the shortest path between the all pairs of vertices, and it is a measure of the overall routing efficiency of the network. To assess the small-worldness of the constructed functional networks, we simulated 1,000 random networks via random rewiring using the same number of nodes, mean degree and degree distribution of the real network, and calculated the mean clustering coefficient To characterize the topological organizations of weighted functional networks, the following graph theoretical measures were evaluated: clustering coefficient, characteristic path length, small-worldness, assortativity, and nodal strength. The clustering coefficient  Newman, :T is the number of the links in the network, aij is the corresponding element of the adjacency matrix between nodes i and j, and ki is the degree of node i. In general, r lies between −1 and 1. An assortative network has positive assortativity, which indicates a network with more mutually coupled hub nodes, whereas r < 0 implies a disassortative network in where high-degree hub nodes are unlikely to be inter-linked  were characterized using nonparametric permutation tests. For each network metric, we first calculated the between-group difference in terms of the mean network metrics. Then, we randomly reallocated all values into two groups and recomputed the mean differences between the two randomized groups. The permutation was repeated 10,000 times, and the 95th percentiles of the empirical distribution were used as the critical values for a two-tailed p < 0.05, FDR-corrected).Regarding the multiple exploratory analyses of the nodal measures, the multiple comparisons correction was performed for nodal strength (https://sites.google.com/site/bctnet/comparison/nbs) approach. A primary threshold was applied to keep a subset of supra-threshold links. Then a corrected p-value was computed using the null distribution of the maximal connected component size, which was empirically derived from 10,000 nonparametric permutations. Further details of the NBS approach can be found in the prior work  in terms of motion, the “scrubbing” method was used to substitute the frames with frame-wise displacement over 0.5 mm by linearly interpolating neighboring frames , and no between-group differences were found in the duration of disease , MMSE , H&Y score , or UPDRS score . Compared with familial PD groups, sporadic PD patients scored higher in HAMD , which suggested sporadic patients showed more severe depression and anxiety.Demographic information, duration of disease, disease stage, UPDRS score and medications are summarized in Table p = 0.034) on the fourth wavelet scale (0.016 −0.031 Hz) . Moreover, as shown in Figure Using the NBS analysis, five decreased functional connections were revealed in the familial PD group on the fourth scale, under the primary threshold (p < 0.05) in the left amygdala .As shown in Table On the fourth wavelet scale, compared with healthy control, sporadic PD patients also showed decreased assortativity. However, no significant difference in network properties was found between familial PD group and healthy controls see .In this work, we explored the differences of topological organizations of the functional brain networks of subjects with sporadic and familial PD, and shed some light on the pathological mechanism impaired by this neurodegenerative disease. Compared to familial PD patients, altered functional connectome among sporadic PD group was found to predominant in the frequency band between 0.016 and 0.031 Hz, which is within the frequency range (0.01–0.073 Hz) of gray matter-related oscillations  was found to be the hub of altered connections. Those changed connections were between the left amygdala and regions related to the retrieval of motion information, e.g., the bilateral fusiform gyrus , left caudal lingual gyrus, right rostral lingual gyrus, and right V5/MT+ area (Ueno et al., To our knowledge, our work is the first to explore frequency-dependent functional topological alterations between sporadic PD and familial PD. The difference of connectomes between sporadic PD and familial PD was identified and its pathological indication was discussed. Our findings not only provide novel insight into the neuropathological mechanisms of sporadic PD and familial PD but also highlight the potential clinical significance of frequency-dependent functional topology associated with different forms of PD.YT, BT, and JG contributed to the conception and design of the study. LM, CW, ZL, and WL conducted clinical assessments and MRI data collection. YT, XX, and CW performed data analysis. YT, JG, and HX wrote and critically revised the manuscript. JG takes full responsibility for the work and decision making.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Tumor-related stroma plays an active role in tumor invasion and metastasis. The tumor–stroma ratio (TSR) in the pathologic specimen has drawn increasing attention from the field of predicting tumor prognosis. However, the prognostic value of TSR in solid tumors necessitates further elucidation. We conducted a meta-analysis on 14 studies with 4238 patients through a comprehensive electronic search on databases updated on May 2016 to explore the relationship between TSR and prognosis of solid tumors. The overall hazard ratio showed that rich stroma in tumor tissue was associated with poor overall survival (OS)  and disease-free survival (DFS)  of patients with solid tumors. The effect of low TSR on poor OS was observed among various cancer types, but not in the early stage of cervical caner. A significant relationship between low TSR and poor OS was also observed in the subgroup analyses based on study region, blinding status, and Newcastle–Ottawa Scale (NOS) score. Subgroup analyses indicated that cancer type, clinical stage, study region, blinding status, and NOS score did not affect the prognostic value of TSR for DFS. Moreover, low TSR was significantly correlated with the serious clinical stage, advanced depth of invasion, and positive lymph node metastasis. These findings indicate that a high proportion of stroma in cancer tissue is associated with poor clinical outcomes in cancer patients, and TSR may serve as an independent prognostic factor for solid tumors. Tumor aggression is considered to be a multifactor process and is significantly influenced by the microenvironment. As the main component of a tumor microenvironment, the stromal compartment is essential for the maintenance of epithelial tissues and their malignant counterparts. Changes in the epithelium and the stroma frequently occur concurrently. The stroma could actually act as a barrier in tumorigenesis by restraining tumor cell proliferation in a normal tissue. However, cancer-related stromal components could actively facilitate the growth, differentiation, and movement of cancer cells in a tumor tissue . In the In the past decades, the main target for therapeutic interventions was solely based on the characteristics of the tumor cells. However, tumor-related stroma could also provide valuable information . When tuThe process of literature selection was shown in Figure P = 0.012; random effects), advanced depth of invasion , and positive lymph node metastasis . This finding indicated that a rich stroma in a tumor tissue may promote tumor invasion and aggressiveness. However, no association existed between TSR and certain factors, such as gender , tumor size , histological grade , and lymphatic or venous invasion .The relationship of TSR with clinicopathological features are illustrated in Table P < 0.001; random effects) , NSCLC , HCC , BC , EC , and other cancers , but not with CC  , I–III , and Stages II–III , but not for Stages I–II . Furthermore, this association did not only exist in the Eastern Asian population , but also in the European population   highly increased the risk of shortening the OS . Thus, we used a random-effects model to estimate the overall HR for OS. When the subgroup analysis was conducted to assess the source of heterogeneity based on cancer type, clinical stage, study region, blinding status, sample size, and NOS score, the heterogeneity was obvious to be still significantly evident  , CRC , EC , and other cancer types  . When subgroup analysis was performed, the heterogeneity was slightly reduced, but still statistically significant , clinical stage (P = 0.829), study region (P = 0.172), blinding status (P = 0.764), sample size (P = 0.478) and NOS score (P = 0.079) did not contribute to the source of heterogeneity for OS. Moreover, the data demonstrated that cancer type (P = 0.685), clinical stage (P = 0.811), study region (P = 0.432), sample size (P = 0.489) and NOS score (P = 0.098) did not account for the source of heterogeneity for PFS.We conducted meta-regression analysis to investigate the potential source of heterogeneity among studies for OS and PFS. The results showed that cancer type  and DFS . Moreover, the shape of the funnel plots did not show any obvious evidence of asymmetry for the outcomes of OS  stained sections, was first proposed by Mesker  in CRC pIn this study, we conducted a meta-analysis on 4238 patients to evaluate the relationship between TSR and the prognosis of solid tumors, as the clinical implications of TSR remain unknown. From the statistical results, a high proportion of stroma to tumor parenchyma is significantly correlated to unfavorable prognosis (OS and DFS) in solid cancers. Thus, TSR could be an independent predictor of OS/DFS in patients with solid cancers. However, given that the biological characteristics of different tumors were varied, extra cautions is necessary when applying these results into clinical practice. We analyzed the combined HR through subgroup analysis on the basis of cancer types to further investigate the predictive value of TSR in various types of tumors. When the analysis was restricted to the survival outcome of DFS, a positive result was observed in all types of cancers, thus indicated that TSR could be a useful predictor for disease-specific mortality in patients with solid tumors. When the analysis was focused on OS, low TSR was observed to be significantly associated with worse survival in CRC, NSCLC, HCC, BC, EC, and other cancer types. Therefore, TSR can serve as a novel index for the prediction of all-cause mortality in solid tumors abovementioned. However, a borderline relationship was observed between TSR and poor OS in patients with CC. Similarly, the predictive value of TSR associated with the prognosis of solid tumors on an early stage (I–II stage) was determined to be insignificant, which also belonged to CC according to the subgroup analysis of the clinical stage. Thus, the prognostic role of TSR for early stage CC (I–II stage) appeared to be weakly supported by evidence. The consolidated results of Pongsuvareeyakul et al.  focused P < 0.05), indicating that our results were stable and reliable.Although sensitivity analysis supported the robustness of our results, the findings must be cautiously interpreted. In our meta-analysis, the heterogeneity of the OS and the DFS estimations were extreme, even when we conducted subgroup analyses. The marked heterogeneity could be probably attributed to the differences in the characteristics of the patients, cancer types, ethnicity, study protocol, and literature quality. Considering the confounding effect of such differences, we conducted a meta-regression analysis to further investigate the source of heterogeneity. However, none of the aforementioned confounding effects could completely explain the heterogeneity. Instead, we used a random-effects model to calculate the consolidated results to minimize the influence of heterogeneity to certain degree . MoreoveWe analyzed the correlation between TSR and the clinicopathological features that affected the survival outcomes of the cancer patients to further investigate the prognostic impact of TSR on solid tumors. According to our pooled results, the abnormal proportion of TSR was significantly associated with certain clinical parameters, such as clinical stage, depth of invasion, and lymph node metastasis. These results strongly supported the negative value of rich stroma on poor outcomes in solid tumors and tumor-related stroma plays a promoting role in tumor progression through different pathways. However, the mechanism underlying the promoting effect of stroma in solid tumors is still not fully understood.During the early stage of tumor invasion, tumor cells penetrate the basement membrane and activate the stromal cells to form the tumor microenvironment . AlthougThe components of tumor-related stroma are complex, including the extracellular matrix (ECM), various cell types, and different secreted factors. As an intermediary, the ECM assists the communication of cancer cells with stromal cells, such that cancer cells are able to colonize the microenvironment and form a metastasis . EvidencAlthough significant progress has been attained in these current studies, certain limitations still persist in the clinical practice for TSR. First, the evaluation of TSR is mainly conducted postoperatively, and whether the TSR estimated from the preoperative biopsy specimens represents the entire tumor tissue is still uncertain. Although high consistency has been observed between the results of pre-operation and post-operation in a study of esophageal adenocarcinoma , the eviIn summary, our present meta-analysis revealed that rich stroma in tumor tissue is associated with unfavorable prognosis, including OS and DFS, in patients with solid tumors. Given its convenience, quickness, and inexpensiveness in clinical application, TSR could be a useful tool for the prediction of prognosis and outcomes of solid tumors. However, further studies are recommended to explore the clinical importance of tumor-related stroma in tumor formation and development because all included studies in our meta-analysis are retrospective and the mechanism of stroma in tumor progression is still uncertain. In addition, the interactions between stromal components and tumors are critical for tumor aggressiveness and thus must be seriously considered for future novel therapeutic approaches.This meta-analysis was conducted according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) . The PubPublications were regarded as eligible when they satisfy all of the following criteria: (1) the cohort design reported the relationship between TSR and prognostic outcomes of solid tumors, such as OS, and DFS; (2) the patients with solid tumors were divided into two groups, namely, stroma-rich (low TSR or high carcinoma percentage) and stroma-poor (high TSR or low carcinoma percentage), regardless of the cutoff value; (3) the HRs for survival outcomes related to the TSR were provided in the original data or extracted from sufficient information; (4) the articles were written in any language as full papers; (5) articles with the largest patient cohort among duplicated publications by the same authors or institutes were included in the analysis; (6) the papers were not reviews, conference abstracts, editorials, or letters; and (7) the studies did not belong to basic research and animal experiments.Two authors (JYW and CXL) independently reviewed and extracted information from all of the eligible studies according to the criteria of study selection. Any disagreement between the reviewers was resolved by consensus. Data extracted from the studies included the first name of the authors, publication year, study region, cancer type, duration period, follow-up time, sample size, blinding status, cutoff value, clinical features, survival outcomes, HR estimation, and quality scores. Blinding status represented that the evaluation of the TSR was blinded to the clinical outcomes. In studies where the HRs and their corresponding 95% CIs of univariate and multivariate analyses were provided, only the latter was applied to the data synthesis because it is more precise and it considers the confounding factors. In the absence of results from multivariate analysis, HR was extracted from the univariate analysis or calculated using the Kaplan–Meier survival curves .The quality of included studies was assessed by NOS according to the following categories: selection, comparability, and outcome of interest . The totP < 0.05. Subgroup analyses were conducted according to cancer type , clinical stage , study region (“Eastern Asia” and “Europe”), blinding status (“yes” and “none reported”), sample size (“≥ 100” and “< 100”), and NOS score (“≤ 6” and “> 6”). Meta-regression analysis was also performed to determine the potential sources of heterogeneity. For the pooled analysis of the correlation between TSR and clinicopathological features , the ORs and their corresponding 95% CI were combined to estimate the effect. STATA version 11.0  was used for all statistical analysis. All statistical tests were two sided.The combined HR and 95% CI were used to assess the strength of TSR with survival endpoints (OS and DFS) based on the data extracted from the eligible studies. HR > 1 with 95% CI exceeding 1 indicated an increased risk of poor prognosis for patients with stroma-rich tumors. The statistical significance of the pooled HR was determined through Z–test. The results were considered statistically significant if P<0.05. Heterogeneity was also quantitatively estimated using the I2 metric, which is independent from the number of studies used in the meta-analysis  [P<0.05 or I2 > 50%), the pooled HR estimation of each study was calculated using a random-effects model (DerSimonian and Laird method). Otherwise, a fixed-effects model was applied (Mantel–Haenszel method) [P value of less than 0.05 was defined as statistically significance.Heterogeneity assumption was qualitatively examined through the chi-squared test based on the Q statistic, and was considered statistically significant when geneity) . We used method) . Sensiti


NHANES 1999–2006: n = 1150, pregnant women) were examined for this purpose. Children or women with blood lead ≥10 µg/dL or C-reactive protein (CRP) >5.0 mg/L were excluded. ID was defined as total body iron stores <0 mg/kg body weight, calculated from the ratio of soluble transferrin receptor (sTfR) to serum ferritin (SF). The receiver operating characteristic (ROC) curve was used to characterize the sensitivity and specificity of Hb, MCV, and EP measurements in screening for ID. In detecting ID in children three to five years of age, EP (Area under the Curve (AUC) 0.80) was superior to Hb (AUC 0.62) (p < 0.01) but not statistically different from MCV (AUC 0.73). In women, EP and Hb were comparable , and both were better than MCV  (p < 0.01). We concluded that the sensitivity and specificity of EP in screening for ID were consistently superior to or at least as effective as those of Hb and MCV in each population examined. For children three to five years of age, EP screening for ID was significantly better than Hb and similar to MCV. For both non-pregnant and pregnant women, the performance of EP and Hb were comparable; both were significantly superior to MCV.Hemoglobin (Hb), mean cell volume (MCV), and erythrocyte protoporphyrin (EP) are commonly used to screen for iron deficiency (ID), but systematic evaluation of the sensitivity and specificity of these tests is limited. The objective of this study is to determine the sensitivity and specificity of Hb, MCV, and EP measurements in screening for ID in preschool children, non-pregnant women 15–49 years of age, and pregnant women. Data from the National Health and Nutrition Examination Surveys (NHANES) (NHANES 2003–2006:  Worldwide, according to the World Health Organization (WHO) , iron deIn the US, several methods have been used to define ID. The most common of these were the models that classified individuals as ID when abnormal values were present in at least two of three measures of either SF, TS, and EP (the ferritin model) or MCV, TS, and EP (the MCV model) ,9,11. ThIn our earlier analysis  of data NHANES is a multi-purpose survey designed to assess the health and nutritional status of adults and children in the United States. Currently NHANES is a continuous survey (1999–present) that includes an interview in the household followed by a standardized physical examination in a Mobile Examination Center. The NHANES relies on a stratified multistage probability sample that is based on the selection of counties, blocks, households, and persons within households. The surveys were conducted by the National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention (CDC). Ethical approval was obtained by the NCHS Research Ethics Review Board. Written informed consent was obtained from participants 12 years and older. Parental consent was obtained for those under age 18. The procedures for data collection and analysis are published elsewhere ,21,22. In = 379 for children, n = 334 for non-pregnant women, and n = 70 for pregnant women); (ii) elevated blood lead levels ≥10 µg/dL ; or (iii) elevated CRP >5.0 mg/L . Our final sample included 853 children, 3104 non-pregnant women, and 1141 pregnant women. Sincewe did not intend to perform population-based studies, the original sample weights assigned to the data were not included in the analysis. We restricted our study sample to participants who attended physical examinations in Mobile Examination Centers; 1241 children aged three to five years, 3451 non-pregnant women aged 15–49 years, and 1219 pregnant women. We excluded those who had (i) missing SF, sTfR, EP, Hb, MCV, blood lead, or C-reactive protein (CRP) measurements , an automated homogeneous immunoturbidimetric assay, was performed on a Hitachi 912 clinical analyzer  [sTfR and SF assays were conducted at the National Center of Environmental Health (NCEH), CDC for the 2003–2006 specimens and the surplus specimens from 1999 to 2002. In brief, the Tina-quantIN, USA) . The metIN, USA) .® Ferritin immunoturbidimetric assay on the Hitachi 912 clinical analyzer  [SF was measured using two methods. A single-incubation two-site immunoradiometric assay (IRMA)  was used in 2003. However, this assay was discontinued by the manufacturer in early 2004, so ferritin was measured by the Roche Tina-quantIN, USA)  in 2004–IN, USA) ,20. Due IN, USA) .v/v) mixture of ethyl acetate-acetic acid and finally back-extracted into diluted hydrochloric acid. The protoporphyrin in the aqueous phase is measured fluorometrically at excitation and emission wavelengths of 404 and 658 nm, respectively. Calculations are based on a processed protoporphyrin IX (free acid) standard curve. After a correction for the individual hematocrit is made, the final concentration of protoporphyrin in a specimen was expressed as micrograms per deciliter of packed red blood cells (μg/dL RBC) [EP was measured at CDC NCEH before 2001 and subsequently at the State of New York Department of Health after 2001. A modification of the acid extraction method originally described by Sassa et al.  and Chis/dL RBC) ,17,18,19/dL RBC) .Hb and MCV were measured as part of a complete blood count in the Mobile Examination Centers using the Beckman Coulter MAXM hematology flow cytometer  ,20,21,22A trimester was based on the number of months pregnant reported by the mother from the surveys. Only females who reported that they were pregnant at the time of the medical examination were asked about their trimester. First, second, and third trimester were defined as less than or equal to three months pregnant, four to six months pregnant, and seven months or more pregnant, respectively. The trimester value for women who did not know, were not asked, or did not report how long they had been pregnant was categorized as unknown.Race/ethnicity was based on self-reported data from the surveys. We excluded comparisons of participants who reported that they were of Latino ethnicity other than Mexican-American and participants who reported they were from more than one race/ethnic group because of the small sample size of these groups. We defined ID as TBI stores <0 mg/kg ,14. Posin = 40): Flowers sSTfR = 1.5 × Roche sSTfR + 0.35 mg/L. We used the original Roche SF concentrations for the total body iron stores calculation because a previous comparison of the Roche assay with the ELISA method used to develop the total body iron stores model [To convert the Roche sSTfR concentrations to those equivalent to the Flowers assay , we apples model ,14 indices model ,16.x = 0, y = 0), the better the performance of the indicator. Statistical Analysis Software (SAS) software  with logistic procedures was used to generate the ROC curves and to test the significance for the areas under the ROC curves (AUC).We used ROC curves  to chara9/L), or of possible liver disease, as defined by at least one of two abnormal elevations  on alanine aminotransferase (>70 U/L) and aspartate aminotransferase (>70 U/L) [First, we examined the ROC performances on overall subjects for children three to five years of age, non-pregnant women 15–49 years of age, and pregnant women. As described above, we excluded subjects with elevated blood lead (>10 μg/dL) or CRP >5 mg/L because elevated EP could be caused by lead poisoning and SF is an acute-phase protein that could be elevated by inflammation. Second, we stratified our analysis by race/ethnicity for children, non-pregnant women, and pregnant women; by gender for children; and by trimester for pregnant women. Finally, because liver disease, infection, and inflammation can influence biochemical indices , we repe>70 U/L) . The demographic and biochemical characteristics of the selected sample of the NHANES data are shown separately for children (three to fiev years of age), non-pregnant women (15–49 years of age), and pregnant women in p < 0.001) but similar to those of MCV , the overall sensitivity and specificity of EP in detecting ID were significantly superior to Hb  the use of a broad sample of U.S. preschool children and women of childbearing age from the NHANES studies; (ii) hematologic and biochemical measurements using carefully controlled and validated assays; (iii) systematic evaluation of the screening performance of the indicators by ROC analysis; and (iv) defining ID as TBI stores <0 mg/kg as calculated from the ratio of sTfR to SF, as used for monitoring ID in the US population . PreviouDespite the many advantages of the use of the ratio of sTfR to SF to define ID ,15,16, sEP was measured in the NHANES studies using a modification of an acidic extraction method . Until t12 or folate deficiency), malaria, and other infections, Hb may be so non-specific as to be of little use in screening for ID [The results of our comparative analysis of EP, Hb, and MCV seem robust for the U.S., where ID is the most common single cause of anemia among young children and women of childbearing age ,38. Cautg for ID ,52. In conclusion, using the criterion for ID of TBI stores <0 mg/kg body weight, as estimated from the ratio of sTfR to SF, the sensitivity and specificity of EP in screening for ID were consistently superior to or at least as effective as those of Hb and MCV in each population examined. For children three to five years of age, EP screening for ID was significantly better than Hb screening and similar to MCV screening. For both non-pregnant and pregnant women, the performance of EP and Hb were comparable; both were significantly superior to MCV. As in our earlier study , we conc


This paper calculates the distribution of an adjusted measure of income that deducts damages due to exposure to air pollution from reported market income in the United States from 2011 to 2014. The Gini coefficient for this measure of adjusted income is 0.682 in 2011, as compared to 0.482 for market income. By 2014, we estimate that the Gini for adjusted income fell to 0.646, while the market income Gini did not appreciably change. The inclusion of air pollution damage acts like a regressive tax: with air pollution, the bottom 20% of households lose roughly 10% of the share of income, while the top 20% of households gain 10%. We find that, unlike the case for market income, New England is not the most unequal division with respect to adjusted income. Further, the difference between adjusted income for white and Hispanics is smaller than expected. However, the gap in augmented income between whites and African-Americans is widening. Blinder, 1980, [With some exceptions, recent research on the distribution of income in the United States has supported two broad conclusions. The first is that with access to richer, more complete, data sets, we now have an accurate sense of that distribution, not least the income that accrues to the “one percent.” The second is that for most measures of dispersion, that distribution has become more unequal over time .much more unequal than recent studies have suggested, but that it might also be becoming more equal over time. These surprising results are a consequence of our view that the improved measurement of income depends not just on better records of market income, but on more comprehensive definitions of income. In particular, our calculations are predicated on the decades old suggestion in Nordhaus and Tobin [We challenge both of these conclusions, and advance two provocative claims; namely, that distribution is nd Tobin  that the2.5) and ground-level ozone (O3), because monitoring networks for these species are relatively broad and mortality risk accounts for the vast majority of adverse impacts from exposure to air pollution when expressed in monetary terms [2.5 and O3 exposure, and then monetizing these risks, using techniques from a long and well-developed literature on the monetization of pollution damage [We focus on one particular nonmarket outcome, the household costs associated with exposure to air pollution. In particular, we focus on mortality damages from fine particulate matter 2.5 and O3 represent a substantial and practical choice. It is no more than a first step, however: it would be interesting to ask, for example, how the distribution of “full income” looks when, say, the costs of ground water pollution are included.Second, while considerable energies in both the academic and public sectors have been devoted to the construction of “satellite accounts,” we believe that our particular focus on fine particulate matter and ground-level ozone is one of several natural “bridges” for the integration, however tentative, of market and non-market accounts. The use of environmental assets and services is, with household production and investments in health and human capital, a first order priority for those committed to the development of augmented accounts , 17 and,distribution, as opposed to level, of enhanced or augmented income finds echoes in Nordhaus’ [Third, our particular focus on the ordhaus’  concept not a consequence of double counting. We calculate residual or extant damage, given reported pollution levels, for all households, including affluent ones. Furthermore, because cross-sectional price variation may reflect spatial differences in public good provision, we use cost-of-living adjusted (COLA) incomes, but note that the COLA adjustment is even higher in high-damage communities.Last, to forestall possible criticism, we observe, at the outset, that while rich households can and do expend some of their income on more expensive houses and other products associated with cleaner air, our estimates are We first use the results for 2011 to describe some cross-sectional patterns that are broadly consistent across each year in our sample period. Next, we consider how outcomes have evolved over time. In order to bolster our use of the top-coded market income data, we estimate a Gini coefficient for household income using all 1.2 million households in the IPUMS database for 2011. The estimated Gini, reported in Table A in Table A in 2.5 and O3. The fitted coefficients are 0.113 (se = 0.001) and 0.091 (se = 0.001) for PM2.5 and O3, respectively. The estimated Gini coefficient for baseline mortality risk is 0.712 (se = 0.002). Our surprising conclusion, therefore, is that while the distribution of individual exposure is reasonably equal, the distribution of risk is not. The latter drives the result for adjusted income. We note, in the discussion section below, that other pollutants are distributed differently across space and among communities. Our focus on PM2.5 and O3 stems from the repeated finding that they contribute the greatest share of damages among all pollutants regulated by the Clean Air Act [This begs the question, why are damages so regressive? The calculation of damages–see Eqs  and 2)  2) in th Air Act ,5.2.5 by income quantile. Furthermore, the exposure is less harmful among higher income groups because mortality rates (baseline risk) are also lower for richer households  of national income; for a substantial number of households in this group, adjusted income is negative. The share of the top quintile increases, however, from 52% to 61%. In short, the bottom quintile loses 10 percentage points while the top quintile gains about 10 percentage points. Pollution damage therefore results in a symmetric and bracingly regressive reallocation of income from the bottom quintile to the top. Consistent with an observation made in the introduction, one way to view this outcome is as a transfer of non-market income. The incorporation of damages from pollution does not change the shares of the middle three quintiles much, however. This implies that the mortality impacts from exposures to PM2.5 and O3 are equitably distributed across households in these income percentiles.Our income share analysis therefore supports the conclusion that the adjustment for pollution damages functions like a regressive tax. It also reveals that the redistribution is itself concentrated. That is, it is not as though the increased share that accrues to the top quantile comes in equal measure from the bottom four quantiles: rather, the burden of this regressive pseudo-tax falls squarely on the shoulders of the poorest 20% of households.more egalitarian since 2011. One reason for this is found in the Gini for damages: the costs of exposure to air pollution became more equally distributed, as reflected in the modest decline of the damage Gini from 0.670 (se = 0.0005) to 0.659 (se = 0.0005). The share decomposition reveals that, while the dominant share of inequality in pollution damage stems from exposure to PM2.5, this share is falling relative to O3 between 2011 and 2014. For 2011 through 2014, O3 levels averaged about 28 parts per billion. In contrast, PM2.5 concentrations fell by 10% from 10.2 to 9.3 (ug/m3). These significant reductions in PM2.5 concentrations imply reductions in emissions of primary PM2.5 in addition to precursors such as sulfur dioxide, nitrogen oxides, ammonia, and non-methane hydrocarbons. Another potentially important factor driving the reduction in the adjusted income Gini coefficients was the enactment of the Affordable Care Act (ACA). This large policy intervention likely affected baseline health status  primarily among the previously uninsured. Communities for which the individual mandate was a binding policy change consist of low-income populations without access to insurance and relatively healthy persons that elected not to purchase insurance policies. To the extent that the ACA affected the adjusted income Gini , it must have predominantly improved the health status among the former—low-income populations without access to insurance.In addition to 2011, The divisional Gini coefficients for adjusted income challenge received wisdom. Viewed from this perspective, the two most unequal regions are, by substantial margins, East South Central  and East North Central , where the Ginis in 2011 were 0.854 and 0.793, respectively. On the other hand, these were also the divisions in which the Gini decreased most, to 0.750 and 0.720, respectively. In contrast, in terms of adjusted incomes, New England (0.657 in 2011 and 0.640 in 2014) is one of the most egalitarian divisions, but has not become substantially more equitable over this period. In short, reckoned in terms of adjusted income, the “heartland,” and not the urban northeast, is the most unequal region of the country, but is becoming somewhat more egalitarian. While a thorough exploration of the causes of regional disparities in the distribution of adjusted income is beyond the scope of this paper, we note that the East South Central and East North Central divisions contain considerable coal-fired power generation capacity and, especially for the East North Central division, heavy manufacturing. Both of these types of production produce copious amounts of air pollution.We provide two perspectives on the racial and ethnic dimensions of income distribution, both of which also challenge the conventional wisdom focused on the market income distribution. We first consider between group differences at a national level. understate the differences between whites and African-Americans, and the extent to which the problem is becoming worse.The differences between whites and African-Americans are even more concerning, however. As is well known, there is an almost 65% difference in household incomes in 2011 and this decreased, to about 62%, by 2014. The difference in adjusted incomes is both larger and increasing: the median white household earned 67% more in 2011, including the costs of air pollution exposure, and this increased to 73% by 2014. In short, standard income measures The second perspective on racial income inequality is on the differences between market and adjusted Ginis within groups. The bottom of and experienced a substantial decrease, to 0.693 (se = 0.003) in 2014. The adjusted income Gini for whites also decreased, although not quite as much, to 0.635 (se = 0.001), while that for Hispanics experienced a still smaller decline, from 0.577 to 0.551 (se = 0.002). Once more, there is something to be learned from the distribution of air pollution: while damage Ginis for the three groups are quite similar, those for African-Americans and whites, declined, while that for Hispanics was more or less unchanged. The improvement in the overall distribution of income reflects, in considerable measure, improvements for whites and African-Americans, among whom the distribution of damages has become more equal. Furthermore, all of this can be attributed to the more equitable distribution of PM2.5-related costs.The distribution of adjusted income within these populations is another matter entirely. The Gini for African-Americans was much higher in 2011  than for either whites  or Hispanics  2.5 exposure reported by LePeule et al., [Table E in  et al.,  is used. et al.,  have recThe bottom panel in Table E in less unequal over time. Within these broadly important results, we also report several more nuanced, yet perhaps equally compelling findings. First, the spatial pattern of inequality in adjusted income differs from that of market income; states in the colloquial heartland exhibit very high degrees of adjusted income inequality whereas states in the urban northeast tend to show the highest extent of market income inequality. However, in the heartland, inequality in adjusted income fell more rapidly than market income. Second, African-American communities exhibit radically higher inequality in adjusted income relative to market income and relative to other racial groups. Among white and African-American households, the measure of inequality in adjusted income fell more rapidly than in households comprised of different races. Third, pollution damage acts like a highly regressive and remarkably concentrated tax. The inclusion of pollution damages reallocates income from the bottom 20% of households to the top 20%. The middle quintiles are essentially unaffected. This result remains robust over time and over different ways of measuring damages.This analysis challenges common perceptions of the distribution of income by estimating an augmented measure of income inclusive of pollution damage. This tack builds on a much earlier literature ,13 to ma3 and PM2.5 are spatially far from complete. Thus, our results are beholden to the design of the pollution monitoring networks and the truncated sample of households in counties with such devices. Finally, we recognize that the focus here is on one set of environmental pollutants. There are, of course, many others including airborne toxics, water pollutants, toxins in the soil, and adverse effects from climate change. Our focus lies on O3 and PM2.5 because of the large damages associated with these species reported in the literature [We note several caveats to our findings. First, as with any attempt to measure pollution damages, our estimates embody considerable uncertainty. For example, both the connection between exposure and mortality ,40 and mterature  and becaWe conclude by arguing that our results reported herein are intended to shift the debate about income inequality from a singular focus on market income to one that is inclusive of phenomena that the economics literature has recognized are important to national welfare for over 40 years.th percentile in each state is replaced with the mean value of all incomes above the 99.5th. We do not correct for topcoding. To bolster this approach we compute Gini coefficients (for market income) using the top-coded data and then compare these to the Gini coefficients reported by the U.S. Bureau of the Census for each of the above years in the sample. Table A in The calculation of Gini coefficients for market income is based on the 2011–14 American Community Surveys (ACS), as provided by the Minnesota Population Center’s Integrated Public Use Microdata Series (IPUMS) . We use All-cause mortality rate data for the same data years are from the U.S. Centers for Disease Control and Prevention . These d3), sulfur dioxide, nitrous oxide, and particulate matter. For this study, we use data on O3 and PM2.5. For a variety of reasons, the monitors do not provide complete spatial coverage. Monitors tend to be located in areas either suspected of being out of compliance or with a track record of being out of compliance with ambient standards. In 2011, there were about 1,300 O3 monitors and 900 PM2.5 monitors. For PM2.5, the monitor count increased to 923 in 2013 and 975 in 2014. For O3, the active monitor count climbed to 1,314 in 2013 before falling back to 1,283 in 2014. Because households are coded by county, and mortality rates are reported by county, we average the air pollution data to the county level and then match, by county, to households and mortality rates.The United States Environmental Protection Agency’s (USEPA) Air Quality System (AQS) monitoring network  provides2.5 and O3. To do so we employ the following approach, which is standard in both the academic literature and it is employed by the USEPA in its Regulatory Impact Analyses for air pollution and in the legislatively mandated benefit-cost analyses of the entire Clean Air Act [a,r,t = baseline all-cause mortality rate, age-cohort (a), location (r), time (t)Among the damages associated with exposure to local air pollution, increased mortality risk contributes the largest share of monetary damage ,5,6,9. A Air Act ,5,6,12. βp = empirically estimated concentration-response parameter for pollutant (p).Cpr,t = ambient concentration of pollutant (p), location (r), time (t).Because of the use of micro data (the IPUMS), we calculate individual mortality risk, rather than spatially aggregated risk among populations within a grid-cell or county as is more commonplace in the literature. We note that, as expression (1) implies, we attribute population age-cohort mortality risk to individuals within that cohort, by county. Also, note that the income attributed to individuals in the sample is total household income.Another implication of expression (1) warrants particular attention: two individuals exposed to the same levels of air pollution will be assigned different mortality risks if they are members of groups with different baseline mortality rates. In somewhat different terms, spatial variation in risk reflects both variation in exposure to air pollution and, to a surprising degree, variation in baseline mortalities, itself a reflection of variation in baseline demographic characteristics.2.5 to adult mortality risk from Krewski et al., [3, we use the results from Bell et al., [The estimated concentration-response coefficients are obtained from the epidemiological literature. Specifically, we use the reported coefficients that link exposure to PM et al.,  and LePe et al., . For the et al., . USEPA [ et al., , 5 and a et al., , 46, 47 3, it assumed a VSL of $8.3 million (for the 1990 income level) or $9.5 million (for the 2010 income level), both expressed in 2011 prices [To monetize mortality risk, we adopt the Value of a Statistical Life (VSL) approach , which e1 prices . For the1 prices . This yi1 prices . We condGiven these considerations, monetary damage across pollutants, incurred by an individual (i) is given by:As the notation hints, our default specification adopts the egalitarian approach of using the same VSL across populations of all ages, incomes, races and genders ,5. HowevAdjusted household income for individual (i) at time (t) is denoted  is its mean and F(Yt) is its distribution function. The generalization admits negative incomes, which, while it changes the range of admissible values and complicates interpretation, is essential for the reason just outlined: adjusted income is sometimes less than zero. The standard errors for our estimated Gini coefficients are bootstrapped.We use the algebraic equivalent of the generalized covariance-based Gini coefficient , as implS1 FileTables A-F.(DOCX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.


Key research suggests that empathy is a multidimensional construct comprising of both cognitive and affective components. More recent theories and research suggest even further factors within these components of empathy, including the ability to empathize with others versus the drive towards empathizing with others. While numerous self-report measures have been developed to examine empathy, none of them currently index all of these wider components together. The aim of the present research was to develop and validate the Empathy Components Questionnaire (ECQ) to measure cognitive and affective components, as well as ability and drive components within each. Study one utilized items measuring cognitive and affective empathy taken from various established questionnaires to create an initial version of the ECQ. Principal component analysis (PCA) was used to examine the underlying components of empathy within the ECQ in a sample of 101 typical adults. Results revealed a five-component model consisting of cognitive ability, cognitive drive, affective ability, affective drive, and a fifth factor assessing affective reactivity. This five-component structure was then validated and confirmed using confirmatory factor analysis (CFA) in an independent sample of 211 typical adults. Results also showed that females scored higher than males overall on the ECQ, and on specific components, which is consistent with previous findings of a female advantage on self-reported empathy. Findings also showed certain components predicted scores on an independent measure of social behavior, which provided good convergent validity of the ECQ. Together, these findings validate the newly developed ECQ as a multidimensional measure of empathy more in-line with current theories of empathy. The ECQ provides a useful new tool for quick and easy measurement of empathy and its components for research with both healthy and clinical populations. Empathy is important for meaningful relationships in the social world. Despite its importance, there is still a lack of consensus in the field about the definition of empathy . Some eaAffective empathy involves experiencing another’s feelings and emotions through recognizing, being sensitive to emotions in others and sharing the emotional experiences of others by having an appropriate affective response to the other person’s situation ,15,17–23Cognitive empathy involves the process of understanding another person’s perspective by adopting another’s point of view. The ability to take another’s perspective is consistent with what has traditionally been termed theory of mind (ToM) ,14,37–42Research in individuals with psychopathy has highlighted that cognitive and affective empathy are at least partly dissociable processes. For example, research has demonstrated that individuals with psychopathy show significant deficits in affective empathy, but intact cognitive empathy ,21,45–49Several researchers have further proposed there are further components within cognitive and affective empathy, which abilities and drives ,55–64. TFurther accounts suggest that empathy may depend on certain contexts where an individual has the drive or interest to engage with others emotionally ,63,64,71Recent research in individuals with psychopathy highlight this further dissociation in empathy, by suggesting significant reductions in the drive to empathize, rather than the ability, compared to controls when participants were presented videos of others experiencing pain and instructed to either try to empathize with others or to just observe . AdolphsNumerous self-report measures of empathy have been developed and validated over the years. However, many measures have been critiqued because the ambiguous definition of ‘empathy’ has led to inconsistent interpretations of findings within the literature ,28. SomeOther measures focus on measuring the affective component of empathy. The Questionnaire Measure of Emotional Empathy (QMEE) specifically aim to measure an individual’s tendency or drive to respond to another’s emotions and feelings . MehrabiThe Interpersonal Reactivity Index (IRI) ,83 is onAnother popular self-report questionnaire of empathy is the Empathy Quotient (EQ), which was developed to index empathy in both the general population and in different clinical samples, such as autism spectrum disorders (ASD) . The EQ Evidence has shown there are sex differences in empathy, with females showing superiority compared to males. Multiple studies highlight female superiority on self-reported empathy measures, such as the EQ ,94. InteThe main aim of the current study was to develop and validate the Empathy Components Questionnaire (ECQ), a new self-report empathy measure that aimed to include further components more in-line with current theories about empathy. These components included cognitive and affective aspects, along with the ability and the drive to empathize within each of these components. This research consisted of two studies. Study one aimed to develop a quick and easy to administer instrument that measures empathy and its proposed further components. Study one used a principal component analysis (PCA) to identify the factor structure of the ECQ and reduce its length to its core components. Study two included a second sample of participants that completed the refined ECQ. A confirmatory factor analysis (CFA) was used to verify the factor structure generated by the PCA in Study one. Study two also assessed sex differences across all components of empathy. An independent measure of social behavior was also included in study two to examine convergent validity of the ECQ.It was hypothesized that there would be components extracted from the ECQ capturing cognitive and affective components of empathy, along with potential further ability and drive dissociations within each component. This would produce a model consisting of cognitive ability, cognitive drive, affective ability, and affective drive, in-line with recent theories of empathy ,55,57–64The sample included 101 University students and staff  consisting of 66 females  and 35 males . They were recruited by opportunity sample from the University of Bath through various online social networks , active personal recruitment on campus, electronic noticeboards on the University websites, and using campus noticeboards from a variety of departments to provide a wide academic background . The majority of the sample (74.2%) identified as White British, and the remaining participants either reported as Asian or Asian British (3.0%), of a mixed ethnicity (2.0%), of any other ethnicity (2.0%), or preferred not to say (18.8%). 89 participants also reported English as their native language. None of the participants reported having a clinical diagnosis, which was an exclusion criterion.The ECQ was constructed to measure further aspects of empathy including cognitive and affective components, and to further delineate the drive and ability within these components. The first step was to choose a number of items that indexed cognitive or affective components of empathy and determine how effectively they indexed cognitive and affective empathy. Potential items for the ECQ were derived from five popular questionnaires of empathy within the field which incorporate items measuring both cognitive and affective empathy, in order to benefit from the strength of well-validated and established measures ,107. TheThe validity of these 89 empathy items was initially evaluated by four researchers from the Department of Psychology who judged whether each item measured either cognitive empathy (perspective-taking), affective empathy , or neither of these . The raters were provided with definitions of each category based on those provided by Lawrence et al.  and rateItems were further categorized into ability and drive within each component. The same four researchers predicted that items measured one of the four components: cognitive ability, cognitive drive, affective ability, or affective drive. These items were categorized based on the following definitions. Cognitive ability was defined as the skill, capacity or potential in perspective-taking and to adopt another’s point of view; cognitive drive was defined as the motivated interest or tendency in perspective-taking and to adopt another’s point of view; affective ability was defined as the skill, capacity or potential in recognizing, being sensitive to and sharing others’ emotional experiences; and affective drive was defined as the motivated interest or tendency in recognizing, being sensitive to and sharing others’ emotional experiences. These definitions were developed based on previous literature suggesting that individuals may vary in their abilities to empathize compared to their drive to empathize, such as individuals with ASD ,64,112. A four-point response scale (1 = Strongly Disagree to 4 = Strongly Agree) was chosen in order to capture the differences in responses. The current study incorporated a four-point Likert-type scale to avoid true neutral responses and to avoid social desirability bias . This waEthical approval for the present study was obtained from the Psychology Department Research Ethics Committee of the University of Bath, and all participants gave informed consent.All participants completed Study one online via Bristol Online Survey (BOS). Participants viewed each question individually on a computer screen, and then rated the scale about how much they agreed or disagreed with each statement. There was no time limit, and participants took approximately 10 minutes to complete the study. After testing was completed, all participants were debriefed on the nature and purpose of the study.In order to explore the factor structure of the initial version of the ECQ, a PCA using orthogonal varimax rotation was run using SPSS statistical software . PCA is a statistical variable reduction technique used to explore and analyze various dimensions within a dataset and extract meaningful underlying variables –117. Trat (99) = 0.36, p = 0.72.There was no difference in age between males  and females , 2 of 72.06 (df = 39) and a significance criterion p-value of 0.001 resulted in the identification of no significant multivariate outliers. Hence all 101 cases were included for PCA.There were no missing values in the data. None of the ECQ items had skew of a magnitude of +/-2.0 or higher, which is the recommended cut-off criteria ,126. A vp < 0.001), indicating that PCA is appropriate for this dataset [Inspection of the correlation matrix revealed items from the initial ECQ correlated fairly well, with minimum correlation coefficients of 0.10. Multi-collinearity was also not found based on cut-off criteria of 0.90 ,115. The dataset ,118,130. dataset –116,118.The PCA using orthogonal varimax rotation revealed the presence of eleven components, with eigenvalues exceeding a Kaiser’s criterion of 1, explaining 69.20% of the total variance. Orthogonal rotation was chosen based on the theoretical assumption cognitive and affective empathy are partially dissociable components. Varimax rotation also allows for easy interpretation of factor loadings as it maximizes the amount of variance of items and leads to a smaller number of large loadings for each factor ,131,132.r = 0.52, p < 0.00001), and only a small number of items loaded onto the sixth factor (3 items). Due to this significant overlap between the components and the small number of items, it was decided to combine them into one overarching component. This left five components with at least three items and loadings equal to, or above, 0.40. These five components were defined as follows based on previous theories and literature suggesting further components of empathy  = -4.41, p < 0.0001. In order to assess sex differences across all five components in further detail, a between-subjects MANOVA with sex  as the independent variable and the preliminary five ECQ components as the dependent variables was also conducted (T (0.36), F  = 6.82, p < 0.0001, partial eta squared = 0.26. Post-hoc univariate analyses revealed a statistically significant effect between sex and scores on affective ability  = 12.03, p ≤ 0.001, partial eta squared = 0.11), affective drive  = 5.42, p = 0.02, partial eta squared = 0.05), and affective reactivity  = 30.54, p < 0.0001, partial eta squared = 0.24). There were no statistically significant differences between females and males on cognitive ability  = 1.28, p = 0.26) or cognitive drive  = 0.29, p = 0.59)  and females , onducted . Results.59) see .The results from Study one revealed the ECQ had a model consisting of the five factors of cognitive ability, cognitive drive, affective ability, affective drive, and a fifth factor named affective reactivity. These findings are consistent with recent theory and research in the field, which conceptualizes empathy as comprising of multiple components ,55,57–64Sex differences were also examined across the preliminary components and revealed females scored significantly higher than males on the affective empathy components. This suggests that females self-reported higher levels of abilities and drives in recognizing and being sensitive to other’s feelings and emotions, as well as emotionally reacting to others’ emotions. These results are consistent with previous research showing females score significantly higher than males on self-report measures of empathy ,105,106.While the results from Study one showed a five-factor model of the ECQ, some issues remained about the nature of the wording in the items. Some items were too similar in wording, and there was an imbalance of positive and negative worded questions. The majority of questions within each factor consisted of positively worded questions. A more balanced mixture of positively and negatively worded questions would avoid the risk of response bias and social desirability . To avoiTo address these concerns, the aims of Study two were to refine the wording in the items of the ECQ and to run a CFA to test the model fit in a larger independent sample. Study two also aimed to further assess differences on the five components and to establish convergent validity of the ECQ with an independent measure of social behavior. For this purpose, the Social Interest Index- Short Form (SII-SF) was included to specifically measure one’s overall drive and willingness to be social. It was expected the ECQ with refined wording would show the same five-factor model fit, which was found in Study One. It was also hypothesized that females would score higher on the affective components compared to their male counterparts, but score similarly on the cognitive components, which was found in Study one. It was hypothesized drives in cognitive and affective empathy, as well affective reactivity, would positively correlate with scores on the SII-SF .The sample consisted of 265 adults recruited via opportunity sampling within the University of Bath campus and the wider general adult community in the UK. Recruitment was undertaken through campus noticeboards, online research recruitment sites  and through online social networks . Forty-five of the participants were removed because they self-reported having a psychiatric diagnosis, which was an exclusion criteria. Two participants were also removed on the basis of incomplete data sets. A further seven outliers  were removed based on calculated distances outside of the normally distributed data. This left a total of 211 participants whose data was included in the final dataset . This sample was comprised of 116 females  and 95 males , with approximately half being University students (N = 100) and the others being non-students in employment (N = 111). Students were also recruited from a variety of departments and faculties to provide a wide academic background . 195 participants also reported English as their native language. Demographics of participants for Study two are outlined in The wording of the ECQ items was refined for three main reasons. Firstly this would align questions better with the components found in Study one and to further distinguish between them. Clearer questions intended to better reflect specific components, particularly highlighting dissociations between ability and drive, would allow for less ambiguity . The refThe refined ECQ consisted of a total 30 items. The original version of the ECQ contained 28 items taken from the initial ECQ questionnaire used as core items ,83 to beECQ items were refined based on definitions of abilities and drives, in order to align items even better to components of empathy. Both positively and negatively words were also used in the rewording of the items. Positive words measuring the construct of ability in the present rewording the items included: well, able, good and success. Negative words measuring the construct of ability in the rewording of items in the present research included: poor, not very good, unsuccessful and unable. Positive words measuring the construct of drive in the present rewording of the items included: desire, interested, motivate, tend, strive, like, enjoy and willing. Negative words measuring the construct of drive in the rewording of the items included: uninterested, avoid, unaffected and not motivate. During the refinement process, three researchers compared the original questionnaire items with the proposed refined items including the key ability and drive words. The questions were then reworded to include these key item words. After the rewording of the items, the 30 items were rated on how well the items from the refined ECQ with the new words for ability and drive matched the original definition provided for each component by three researchers. In cases of disagreement between the raters, items were further re-constructed, rated and discussed see .A four-point response scale (1 = Strongly Disagree to 4 = Strongly Agree) was used to measure the differences in responses. Negatively worded items were reversed scored (1 = Strongly Agree to 4 = Strongly Disagree). The DVs were scores for each component of the ECQ and the total cumulative ECQ score.In addition to the refined ECQ, participants in Study two completed the following measure for the purposes of convergent validity.The short form of the Social Interests Index (SII-SF) is a 14-item self-report measure that assesses a sense of social feeling toward friendship, love and work, with questions including ‘My friends are very important to me’ and ‘I have warm relationships with some people’ . This meEthical approval for the present study was obtained from the Psychology Department Research Ethics Committee of the University of Bath, and all participants gave informed consent.Participants completed Study two online via Bristol Online Survey (BOS). Similarly to study one, participants viewed each question individually on a computer screen, and then rated the scale about how much they agreed or disagreed with each statement. There was no time limit for each question, and participants took approximately 15 minutes to complete the study. After testing was completed, all participants were debriefed on the nature and purpose of the study.CFA was used to confirm the five-factor structure of the ECQ in this independent sample. CFA theoretically differs from exploratory factor analysis or PCA and is more useful in testing a specific theory because the theory is directly tested by the analysis ,118,122.There were no missing data, and univariate and multivariate outliers that were three standard deviations or greater away from the mean were excluded . The five-factor structure suggested by the PCA in Study one was tested using a CFA by utilizing Analysis of Moment Structures (AMOS 7.0) ,149,150.2, the goodness-of-fit index (GFI), the adjusted goodness-of-fit index (AGFI), the comparative fit index (CFI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). For the current study, multiple criteria were used because each index has different strengths and weaknesses in assessing goodness-of-fit between a particular model and the observed data. For instance, the use of chi-square χ2 is widely used to assess overall good model fit. However, chi-square χ2 is sensitive to sample size, which can lead to inaccurate probability levels and misinterpretations [2 solely on its own, additional fit statistical tests were developed and included in the current analyses. Based on the recommendations of various researchers within the field [Numerous tests were used to assess the goodness of fit of the proposed model. Following the recommendations of Tabachnick & Fidell , Thompsoetations ,155,157.he field ,158,159,he field ), AGFI >he field .2 (395) = 754.08, p < 0.001. Given the rejection of the null hypothesis and the limitations of chi-square χ2, additional and more practical fit indexes were implemented and reviewed in determining the fit of the first measurement model. Some goodness-of-fit tests approached suitable levels , while other goodness-of-fit tests suggested poorer fit . CFI and AGFI results trended towards acceptable fit.Maximum likelihood estimation was employed to estimate all models. This estimation was appropriate to use given the multivariate normality of the current sample and its appropriate size ,152. Thep = 0.05) compared to other significant estimates (p < 0.01). The standardized regression weights can be interpreted as the correlation between the observed variable and factor. For this model, the item ECQ12 had a low regression weight estimate of 0.16 loaded onto cognitive drive. It should also be noted the item ECQ11 had a low regression weight estimate of 0.16 loaded onto affective reactivity (p = 0.04). This suggests that both items do not highly measure the value dimensions compared to the remaining items loaded onto the identified factors. Instead, both items may be measuring different aspects of social functioning than previously intended. Consequently, both items were removed from the measurement model and the analysis was re-run.Post-hoc model modifications were performed in an attempt to develop a better fitting and more parsimonious model. Initially, it was decided to assess the standardized regression weights, also known as factor loadings, in order to amend the measurement model (see Table A in 2 (340) = 611.28, p < 0.001; GFI = 0.84; AGFI = 0.80; CFI = 0.86; RMSEA = 0.062; PCLOSE = 0.001; SRMR = 0.075). These results suggest the model fit was tolerable but further improvement to the model could be done. One way to further refine the measurement model was to look at modification indices. AMOS can estimate the improvement in the model fit χ2 by freeing a previously fixed parameter to be estimated. Fixed parameters with larger modification indices are the leading candidates in identifying misspecifications of a measurement model in order to improve model fit. By freeing certain fixed parameters through additional paths, these relationships indicate they are estimated rather than fixed. High modification indices of at least 10 should be considered for improving the measurement model fit if there are clear theoretical justifications in doing so [The second measurement model of the ECQ revealed improved model fit  and ECQ17 (e12) were correlated. These items were ‘I am uninterested in putting myself in another’s shoes if I am upset with them’ (ECQ21) and ‘I strive to see how it would feel to be in someone else’s situation before criticizing them’ (ECQ17). Both items theoretically measure the motivation to perspective-take and whether one imputes their own judgments in doing so. Arguably these two items may strongly relate with one another based on the overall tendency in cognitive empathy . This mo2 (339) = 597.23, p < 0.001; GFI = 0.84; AGFI = 0.81; CFI = 0.87; RMSEA = 0.06; PCLOSE = 0.02; SRMR = 0.0745). There was also interest in examining the standardized residual covariance matrix. This shows the differences between the sample covariance and the model-implied covariance. With a correct model, most standardized residuals should be less than two in absolute value [The third measurement model of the ECQ revealed further improved model fit  = 502.36, p < 0.001; GFI = 0.85; AGFI = 0.82; CFI = 0.90; RMSEA = 0.05; PCLOSE = 0.24; SRMR = 0.0642). This model exemplified good fit of the refined ECQ and no further modifications were deemed necessary. See The fourth measurement model of the ECQ revealed good model fit , Pearson correlations revealed cognitive ability was positively correlated with cognitive drive , affective ability , affective drive  and affective reactivity . Cognitive drive was also positively correlated with affective ability , affective drive  and affective reactivity . Similarly, affective ability was positively associated with affective drive  and affective reactivity . Lastly affective drive correlated positively with affective reactivity .Relationships were examined between all components within the refined ECQ to better understand these components of empathetic behavior see . With a t (209) = -4.55, p < 0.0001. Further assessment of sex differences in the ECQ were examined using a between-subjects MANCOVA with sex  as the independent variables, age as the covariate, and the five ECQ components as the dependent variables (T (0.11), F  = 4.49, p < 0.001, partial eta squared = 0.10. Post-hoc univariate analyses revealed a statistically significant effect between sex and scores on cognitive drive  = 10.40, p < 0.001, partial eta squared = 0.05), affective ability  = 12.74, p < 0.0001, partial eta squared = 0.06), affective drive  = 12.21, p < 0.001, partial eta squared = 0.06) and affective reactivity  = 19.66, p < 0.0001, partial eta squared = 0.09) when controlling for age. There was also a trending difference between groups on the cognitive ability component when controlling for age  = 2.83, p = 0.09)  and females , ariables . Age was.09) see .The ranges, means, medians and standard deviations for the SII-SF for both males and females are reported in p < 0.0001). Further examination of a histogram for the SII-SF suggested that scores for the SII-SF exhibited significant negative skew data. SII-SF scores were then transformed using an inverse square root transformation to see whether scores were improved. A follow-up Shapiro-Wilk test of normality revealed the transformed SII-SF was no longer statistically significant (p = 0.13). The transformed SII-SF scores were used for the remainder of the analysis. It is worth noting that the interpretation of transformed inverse variables is reversed, where negative relationships are interpreted as positive [Normality for the SII-SF was first assessed through the Shapiro-Wilk test of normality and the examination of histograms  = 3.38, p < 0.05, with an adjusted R2 of 0.02. Introducing the five empathy components from the ECQ explained an additional 30% of variation in SII-SF scores, and this change in the adjusted R2 was statistically significant, F  = 14.93, p < 0.0001, with cognitive ability  and affective drive  as significant predictors SII-SF scores.Findings revealed that at stage one, sex contributed significantly to the regression model, The results using CFA confirmed the five-factor model of the ECQ consisting of cognitive ability, cognitive drive, affective ability, affective drive and affective reactivity, which demonstrated that the five-factor solution provides a good fit, especially after improving the wording of items within the ECQ. Results also showed that females scored higher than males on four out the five components of the ECQ. Females self-reported higher scores on all three affective components of empathy measured through the ECQ compared to males. In addition, there were significant differences on the cognitive drive component and a trending difference on the cognitive ability component. These findings are in-line with extensive evidence that females score significantly higher than males on self-report measures of empathy ,105,106.The aim of the present research was to develop and validate the ECQ, a new brief self-report measure of empathy that produces a total empathy score, as well as scores for a number of relevant components within empathy. The results of study one using a PCA revealed a five-factor model of the ECQ consisting of the components of cognitive ability, cognitive drive, affective ability, affective drive and affective reactivity, and this model was then confirmed using a CFA on an independent sample. The results also showed significant sex differences for all components of affective empathy, with minimal difference between sexes evident in cognitive empathy. Convergent validity was also provided for the ECQ, as scores on the affective drive component of empathy predicted scores on the SII-SF measure of social interest, but not affective ability scores. Cognitive ability scores also predicted scores on the SII-SF. Together, the present results validate the ECQ as a new measure of empathy that indexes various relevant components to provide a useful research tool for investigating empathy in a healthy population and in future in various clinical populations.Empathy theory and research has proposed a multidimensional construct of empathy comprising of cognitive and affective aspects, as well as drive and ability within these components ,64,169. Findings also revealed a further fifth component of empathy interpreted as affective reactivity. Affective reactivity is argued to be action-specific by individuals responding to another’s emotional experiences which often entails sharing these emotions and feelings ,23,24,56The present research also showed that females self-reported having greater empathy overall compared to males, which is consistent with extensive research reporting that females score higher than males on various questionnaire measures of empathy ,105,106.Interestingly, the female advantage in empathy in the present research with the ECQ was most apparent across the affective components, including affective reactivity. However, there were minimal sex differences evident in the cognitive components of the ECQ. These results show that females more naturally tune into the emotional states of others and are more likely to react and respond to other’s emotions and feelings ,19,23,24However the minimal sex differences on the cognitive components of empathy suggest the sexes are less different to each other in the drive and ability to adopt the perspectives of others. This is in-line with other literature using questionnaires of empathy reporting little or no sex differences on some measures of cognitive empathy e.g. ,61,106).,106.61,1Convergent validity of the ECQ was also established in Study two, with higher scores on the affective drive component of the ECQ associated with scores on the independent measure of social interest (SII-SF). This provides some evidence that the drive component of the ECQ is effectively indexing the interest to empathize with others, as both affective drive and SII-SF include items about socially engaging with others. Scores on the cognitive ability component were also associated with scores on the SII-SF, although cognitive drive did not show any association to the SII-SF. No other components from the ECQ predicted scores on the SII-SF. This finding is in-line with evidence suggesting that empathy is partially dissociable, and cognitive and affective empathy may be needed for the same process, while utilizing different regions of the brain for each component . These fThere are some limitations of these studies to be noted. The sample in Study one comprised of students and staff at the University of Bath, which may potentially limit the generalizability of the results to the wider population. However, as previously mentioned, the sociodemographic nature of the sample outlined in Study one was similar to that of other sociodemographic profiles in comparable empathy studies within the literature ,107. StuThe ECQ is a new quick and easy to implement measure of empathy that assesses further components of empathy than previous questionnaires, including cognitive ability, cognitive drive, affective ability, affective drive, and affective reactivity. This five- component model was identified using PCA and then confirmed using CFA in an independent sample. Results further showed that females scored higher than males on four out of the five empathy components, and that the affective drive and cognitive ability components predicted scores on an independent measure of social interest. Together, the present results demonstrate the ECQ to be a valid measure of wider components of empathy, which is consistent with more recent models and ideas about delineating empathy. The ECQ provides a valuable tool for assessing empathy in the general population.S1 File(DOCX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.S2 Data(XLSX)Click here for additional data file.