Factor structure of the Maslach Burnout Inventory Human Services Survey in Spanish urgency healthcare personnel: a cross-sectional study

Background: The Maslach Burnout Inventory (MBI) is an instrument commonly used to evaluate burnout syndrome. The goal of the present study was to assess the internal reliability and the performance of the items and the subscales of the MBI-HSS (the version for professionals working in human services) by validating its factorial structure in Spanish urgency healthcare personnel. Methods: Cross-sectional study including 259 healthcare emergency professionals (physicians and nurses) in the Spanish health region of Lleida and the Pyrenees. Burnout was measured using the Spanish validated version of the MBI-HSS. Internal reliability was estimated using Cronbach’s alpha coefcient. The sampling adequacy was assessed using the Kaiser-Meyer-Olkin measure along with the Bartlett’s test of sphericity. A principal axis exploratory factor analysis with an oblique transformation of the solution and a confrmatory factor analysis with maximum likelihood estimation were performed. Goodness-of-ft was assessed by means of the chi-square ratio by the degrees of free dom, the standardized root mean square residual (SRMR), the root mean square error of approximation (RMSEA), the Tucker-Lewis Index (TLI) and the comparative ft index (CFI). Results: The three subscales showed good internal reliability with Cronbach’s alpha coefcients exceeding the critical value of 0.7. Exploratory factor analysis revealed fve factors with eigenvalues greater than 1. Nevertheless, confrmatory factor analysis showed a relatively satisfactory ft of the three-factor structure (χ2 /df=2.6, SRMR=0.07, RMSEA=0.08, TLI=0.87, CFI=0.89), which was improved when several items were removed (χ2 /df=1.7, SRMR=0.04, RMSEA=0.05, TLI=0.97, CFI=0.98). Conclusions: Although it is necessary exploring new samples to get to more consistent conclusions, the MBI-HSS is a reliable and factorially valid instrument to evaluate burnout syndrome in health professionals from the Spanish emergency services

Empathy and big five personality model in medical students and its relationship to gender and specialty preference: a cross-sectional study

Background:Given the influence that personality can have on empathy, this study explores the relationshipbetween empathy and personality, using three different measures of empathy, and taking into account gender andspecialty preference.Methods:Cross-sectional study. One hundred and ten medical students completed the Jefferson Scale of PhysicianEmpathy, the Interpersonal Reactivity Index, the Empathy Quotient, and the NEO-FFI Big Five personality model.Multivariable linear regression was performed to assess the association between personality traits and empathy.Results:Empathy scales showed weak and moderate correlation with personality. The strongest correlations wereobserved between IRI-Fantasy and Openness, and between IRI-Personal Distress and Neuroticism. Gender andspecialty preference can modify this relationship. The extreme groups of Empathy Quotient had significantdifferences in most personality traits.Conclusions:This study confirmed that empathy is related to personality. Using three empathy scales allowspersonalizing the evaluation of different empathy models and its relation with personality. These results can help todesign programs to study if some personalized intervention strategies could improve the empathy in medicalstudents

An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain

Background: The aim of this study was to evaluate the calibration and discriminatory power of three predictive models of breast cancer risk. Methods: We included 13,760 women who were first-time participants in the Sabadell-Cerdanyola Breast Cancer Screening Program, in Catalonia, Spain. Projections of risk were obtained at three and five years for invasive cancer using the Gail, Chen and Barlow models. Incidence and mortality data were obtained from the Catalan registries. The calibration and discrimination of the models were assessed using the Hosmer-Lemeshow C statistic, the area under the receiver operating characteristic curve (AUC) and the Harrell’s C statistic. Results: The Gail and Chen models showed good calibration while the Barlow model overestimated the number of cases: the ratio between estimated and observed values at 5 years ranged from 0.86 to 1.55 for the first two models and from 1.82 to 3.44 for the Barlow model. The 5-year projection for the Chen and Barlow models had the highest discrimination, with an AUC around 0.58. The Harrell’s C statistic showed very similar values in the 5-year projection for each of the models. Although they passed the calibration test, the Gail and Chen models overestimated the number of cases in some breast density categories. Conclusions: These models cannot be used as a measure of individual risk in early detection programs to customize screening strategies. The inclusion of longitudinal measures of breast density or other risk factors in joint models of survival and longitudinal data may be a step towards personalized early detection of BC.This study was funded by grant PS09/01340 and The Spanish Network on Chronic Diseases REDISSEC (RD12/0001/0007) from the Health Research Fund (Fondo de Investigación Sanitaria) of the Spanish Ministry of Health

Assessing the impact of early detection biases on breast cancer survival of Catalan women

Survival estimates for women with screen-detected breast cancer are affected by biases specific to early detection. Lead-time bias occurs due to the advance of diagnosis, and length-sampling bias because tumors detected on screening exams are more likely to have slower growth than tumors symptomatically detected. Methods proposed in the literature and simulation were used to assess the impact of these biases. If lead-time and length-sampling biases were not taken into account, the median survival time of screen-detected breast cancer cases may be overestimated by 5 years and the 5-year cumulative survival probability by between 2.5 to 5 percent units

Women’s preference to apply shared decision-making in breast cancer screening: a discrete choice experiment

ObjectiveTo analyse women's stated preferences for establishing the relative importance of each attribute of shared decision-making (SDM) and their willingness to pay (WTP) for more participatory care in breast cancer screening programmes (BCSP). DesignA discrete choice experiment was designed with 12 questions (choice tasks). It included three attributes: 'How the information is obtained', regarding benefits and harms; whether there is a 'Dialogue for scheduled mammography' between the healthcare professional and the woman; and, 'Who makes the decision', regarding participation in BCSP. Data were obtained using a survey that included 12 choice tasks, 1 question on WTP and 7 socioeconomic-related questions. The analysis was performed using conditional mixed-effect logit regression and stratification according to WTP. SettingData collection related to BCSP was conducted between June and November 2021 in Catalonia, Spain. ParticipantsSixty-five women aged between 50 and 60. Main outcome measuresWomen's perceived utility of each attribute, trade-off on these attributes and WTP for SDM in BCSP. ResultThe only significant attribute was 'Who makes the decision'. The decision made alone (coefficient=2.879; 95% CI=2.297 to 3.461) and the decision made together with a healthcare professional (2.375; 95% CI=1.573 to 3.177) were the options preferred by women. The former contributes 21% more utility than the latter. Moreover, 52.3% of the women stated a WTP of Euro10 or more for SDM. Women's preferences regarding attributes did not influence their WTP.ConclusionsThe participant women refused a current paternalistic model and preferred either SDM or informed decision-making in BCSP

Bayesian joint ordinal and survival modeling for breast cancer risk assessment

We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model. Time-to-event is modeled through a left-truncated proportional-hazards model, which incorporates information of the longitudinal marker as well as baseline covariates. Both longitudinal and survival processes are connected by means of a common vector of random effects. General inferences are discussed under the Bayesian approach and include the posterior distribution of the probabilities associated to each longitudinal category and the assessment of the impact of the baseline covariates and the longitudinal marker on the hazard function. The flexibility provided by the joint model makes possible to dynamically estimate individual event-free probabilities and predict future longitudinal marker values. The model is applied to the assessment of breast cancer risk in women attending a population-based screening program. The longitudinal ordinal marker is mammographic breast density measured with the Breast Imaging Reporting and Data System (BI-RADS) scale in biennial screening exams. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.Peer ReviewedPostprint (author's final draft

Cost-effectiveness and harm-benefit analyses of risk-based screening strategies for breast cancer

The one-size-fits-all paradigm in organized screening of breast cancer is shifting towards a personalized approach. The present study has two objectives: 1) To perform an economic evaluation and to assess the harm-benefit ratios of screening strategies that vary in their intensity and interval ages based on breast cancer risk; and 2) To estimate the gain in terms of cost and harm reductions using risk-based screening with respect to the usual practice. We used a probabilistic model and input data from Spanish population registries and screening programs, as well as from clinical studies, to estimate the benefit, harm, and costs over time of 2,624 screening strategies, uniform or risk-based. We defined four risk groups, low, moderate-low, moderate-high and high, based on breast density, family history of breast cancer and personal history of breast biopsy. The risk-based strategies were obtained combining the exam periodicity (annual, biennial, triennial and quinquennial), the starting ages (40, 45 and 50 years) and the ending ages (69 and 74 years) in the four risk groups. Incremental cost-effectiveness and harm-benefit ratios were used to select the optimal strategies. Compared to risk-based strategies, the uniform ones result in a much lower benefit for a specific cost. Reductions close to 10% in costs and higher than 20% in false-positive results and overdiagnosed cases were obtained for risk-based strategies. Optimal screening is characterized by quinquennial or triennial periodicities for the low or moderate risk-groups and annual periodicity for the high-risk group. Risk-based strategies can reduce harm and costs. It is necessary to develop accurate measures of individual risk and to work on how to implement risk-based screening strategies.This study was funded by grants PS09/01340 and PS09/01153 from the Health Research Fund (Fondo de Investigación Sanitaria) of the Spanish Ministry of Health. The authors thank the Breast Cancer Surveillance Consortium and the funding that the BCSC received from the National Cancer Institute (U01CA63740, U01CA86076, U01CA86082, U01CA63736, U01CA70013, U01CA69976, U01CA63731, U01CA70040, and HHSN261201100031C). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript