Search Results (3,680)

The problem associated with the manifestation of burnout syndrome is the subject of ongoing interest. In recent years, occupational burnout has been actively studied among professionals in the helping professions (teachers, physicians, social workers, psychologists, prison officers, etc.). However, the phenomenon has been poorly studied among freelancers. Therefore, the aim of this study is to fill this gap by determining the level of burnout syndrome among Bulgarian freelancers and investigate the influence of some work environment factors on professional burnout in the sample. A survey of 1138 freelancers was carried out using the Burnout Self-Assessment Questionnaire developed by Maslach and a questionnaire developed in-house to explore the factors of the occupational environment and ask questions related to socio-demographic factors. Hypotheses are tested using correlation analysis and structural equation modelling. The study reveals high levels of emotional exhaustion (40.91%, n = 484). High values on the scale of depersonalization were reported for 26,3% of the respondents (n = 311). Only 3.1% of the respondents (n = 37) had high values on the reduced personal accomplishment scale. The high levels of burnout among freelancers could be influenced by the specific characteristics of their work environment and the nature of their tasks. Full article

Background/Objectives: Dietary patterns have changed over time, characterising a process of nutritional transition that reflects socioeconomic and demographic inequalities among different populations. This study assessed changes in dietary consumption patterns and the associated social determinants, comparing two time periods in a sample of individuals from a state in the Northeast Region of Brazil. Methods: Data from the 2008–2009 and 2017–2018 Household Budget Survey for the state of Paraíba were analysed, totalling 951 and 1456 individuals, respectively. Foods were categorised according to the NOVA classification and compared based on sociodemographic and economic variables. To determine the factors that most strongly explain the contribution of each NOVA food group to the diet, beta regression analysis was conducted. Results: Differences were observed between the two periods regarding the dietary contribution of the NOVA food groups, with a decrease in consumption of unprocessed foods and an increase in ultra-processed foods. Living in urban areas, being an adolescent, and having an income above the minimum wage were associated with reduced intake of unprocessed foods in both periods. Additionally, being an adolescent and having more than eight years of schooling were associated with higher consumption of ultra-processed foods. Conclusions: The population under study showed changes in food consumption, reflecting a transition process that is occurring unevenly across socioeconomic and demographic groups, thereby reinforcing social inequalities. These findings can guide priorities in food and nutrition policies, highlighting the need for intervention studies to evaluate the effectiveness of such actions. Full article

The population dynamics of high-altitude mountainous areas are shaped by a complex interplay of socioeconomic and environmental drivers. Despite their significance, such regions have received limited scholarly attention. This research identifies and examines the principal determinants of population changes in the high-altitude mountainous zones of Sichuan Province, China. Utilizing a robust quantitative framework, we introduce the Sustainable Population Migration Index (SPMI) to systematically analyze the migration potential over two decades. The findings indicate healthcare accessibility as the most significant determinant influencing resident and rural population changes, while economic factors notably impact urban populations. The SPMI reveals a pronounced deterioration in migration attractiveness, decreasing by 0.27 units on average from 2010 to 2020. Furthermore, a fixed-effects panel regression confirmed the predictive capability of SPMI regarding population trends, emphasizing its value for demographic forecasting. We also develop a Digital Twin-based Simulation and Decision-support Platform (DTSDP) to visualize policy impacts effectively. Scenario simulations suggest that targeted enhancements in healthcare and infrastructure could significantly alleviate demographic pressures. This research contributes critical insights for sustainable regional development strategies and provides an effective tool for informed policymaking. Full article

Background: Pleural mesothelioma (PM) is a type of cancer that is difficult to diagnose and treat. Patients may have vastly varying prognoses, and prognostic factors may help guide the clinical approach. As a recently identified biomarker, the pan-Immune-Inflammation-Value (PIV) is a simple, comprehensive, and peripheral blood cell-based biomarker. Methods: The present study represents a retrospective observational analysis carried out within a single-center setting. Ninety-five patients with PM stages I–IV were enrolled in the study. We analyzed the correlation between patients’ demographic characteristics, clinicopathological factors such as histological subtypes, surgery status, tumor thickness, blood-based parameters, and treatment options with their prognoses. PIV was calculated by the following formula: (neutrophil count × monocyte count × platelet count)/lymphocyte count. Additionally, blood-based parameters were used to calculate the platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and systemic immune inflammation index (SII). Results: We categorized the patients into two groups, low PIV group (PIV ≤ 732.3) and high PIV group (PIV > 732.3) according to the determined cut-off value, which was defined as the median. It was revealed that high PIV was associated with poor survival outcomes. The median follow-up period was 15.8 months (interquartile range, IQR, 7.1 to 29.8 months). The median overall survival (OS) was significantly longer in patients in the low PIV group (median 29.8 months, 95% confidence interval (CI), 15.6 to 44) than the high PIV group (median 14.7 months, 95% CI, 10.8 to 18.6 p < 0.001). Furthermore, the study revealed that patients with low PIV, NLR, and SII values were more likely to be eligible for surgery and were diagnosed at earlier stages. Additionally, these markers were identified as potential predictors of disease-free survival (DFS) in the surgical cohort and of treatment response across the entire patient population. Conclusions: In addition to well-established clinical factors such as stage, histologic subtype, resectability, and Eastern Cooperative Oncology Group (ECOG) performance status (PS), PIV emerged as an independent and significant prognostic factor of overall survival (OS) in patients with PM. Moreover, PIV also demonstrated a remarkable independent prognostic value for disease-free survival (DFS) in this patient population. Additionally, some clues are provided for conditions such as treatment responses, staging, and suitability for surgery. As such, in this cohort, it has outperformed the other blood-based markers based on our findings. Given its ease of calculation and cost-effectiveness, PIV represents a promising and practical prognostic tool in the clinical management of pleural mesothelioma. It can be easily calculated using routinely available laboratory parameters for every cancer patient, requiring no additional cost or complex procedures, thus facilitating its integration into everyday clinical practice. Full article

Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R² (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals. Full article

This study employs k-means clustering to group 30 Chinese provinces into four CO₂ emission patterns, characterized by increasing emission levels and distinct energy consumption structures, and captures their dynamic evolution from 2000 to 2021 using a discrete endogenous Markov chain approach. While Shanghai, Jiangxi, and Hebei retained their original classifications, provinces such as Beijing, Fujian, Tianjin, and Anhui transitioned from higher to lower emission patterns, indicating notable reversals in emission trajectories. To identify the determinants of these transitions, GDP growth rate, population growth rate, and energy investment are incorporated as time varying covariates. The empirical findings demonstrate that GDP growth substantially increases interpattern mobility, thereby weakening state persistence, whereas population growth and energy investment tend to reinforce emission pattern stability. These results imply that policy responses must be tailored to regional dynamics. In rapidly growing regions, fiscal incentives and technological upgrading may facilitate downward transitions in emission states, whereas in provinces where emissions remain persistent due to demographic or investment related rigidity, structural adjustments and long term mitigation frameworks are essential. The study underscores the importance of integrating economic, demographic, and investment characteristics into carbon reduction strategies through a region specific and data informed approach. Full article

This study examined data from participants who completed the SLeep Education for Everyone Program (SLEEP) to explore how various demographic variables affected sleep outcomes and to determine which participant characteristics predicted success. A total of 104 individuals participated. The Sleep Hygiene Index (SHI) measured undesirable sleep behaviors; the Pittsburgh Sleep Quality Index (PSQI) assessed sleep quality and self-reported sleep duration. Participant demographic information was collected at baseline. A mixed ANOVA evaluated group differences, and a multiple linear regression model identified predictors of sleep improvements. Change in SHI scores from pre- to post-intervention demonstrated a significant time × group interaction between Black and white participants (p = 0.024); further analysis indicated Black participants improved more. Better baseline scores predicted more favorable post-intervention outcomes for SHI, PSQI, and sleep duration. Fewer chronic conditions predicted better post-intervention SHI and PSQI scores. Older age also predicted better SHI scores. More favorable initial scores, fewer chronic conditions, and older age were the strongest predictors of positive outcomes following SLEEP. Improved sleep hygiene, sleep quality, and sleep duration were observed over time within subjects across all groups. In summary, SLEEP appears to be effective. Further work exploring challenges experienced by younger participants or those with multiple co-morbidities is warranted. Full article

Background and Clinical Significance: Angioleiomyoma (ALM) is a benign tumor that generally presents as a single lesion and, according to the updated WHO classification, includes the following three histological subtypes: solid (or capillary), cavernous, and venous. Typically, ALMs are described as well-defined nodules in the lower extremities but are unusually located in the acral locations and toes. We summarize two cases of ALM and perform a systematic review to provide foot surgeons with the most up-to-date and useful information on the epidemiological aspects, anatomical distribution, and specific histological subtypes of ALM in the foot. Materials and Methods: A systematic review was carried out according to the criteria of a PICO framework, and a systematic search and data processing were carried out according to the PRISMA guidelines. We analyzed patient demographics, clinical characteristics, diagnostic workup, treatment, and clinical outcomes. Each one of the included articles was independently assessed for methodological quality and risk of bias by an independent evaluator. The risk of bias of the included studies was assessed based on their characteristics. Results: This systematic review included 14 case series with 172 reported cases of ALM. One hundred and seventy-two (18.57%) were cases of ALM located on foot, excluding the ankle region. The female-to-male ratio was 1.48. The most common location was the hindfoot (41.5%), followed by the forefoot (20.2%) and the midfoot (8.9%). In 29.4% of cases, the location of the lesions could not be determined. The most frequent location of the lesions was subcutaneous (69%), followed by subaponeurotic (16.5%) and skin (14.5%) locations. The most frequent histological presentation was the solid histologic subtype (65%), followed by the venous subtype (21%) and the cavernous subtype (14%), respectively. Of the total reported cases of ALM located in foot, 63.1% presented as solid painful lesions. Calcified presentations occurred in 7% of cases, with more than half of the cases located in the hindfoot. Surgical excision was the treatment of choice in the two herein reported cases of solid ALM located in the hindfoot, one of them with a calcified presentation. No recurrence was observed in either case after two and five years of follow-up, respectively. All cases reviewed after surgical excision showed a low recurrence rate with a favorable prognosis regardless of the histological subtype and a very rare tendency toward malignancy. Conclusions: ALMs of the foot present as well-defined, painful nodules in the subcutaneous tissue of middle-aged women. Solid histological subtypes are the most prevalent. Histopathological analysis is usually essential for confirmation. Treatment consists primarily of direct excision, with remarkably low recurrence rates. Full article

According to the literature, the advancement of information and communication technology (ICT) has increased individual exposure to scams, turning fraud victimization into a significant concern. While prior research has primarily focused on socio-demographic predictors of fraud victimization, this study adopts a behavioral perspective that is grounded in the Signal Detection Theory (SDT) to investigate the likelihood determinants of individuals becoming fraud victims. Using survey data of 671 Indonesian respondents analyzed with the Partial Least Squares Structural Equation Modeling (PLS-SEM), we explored the roles of vigilance and financial literacy in moderating the relationship between fraud exposure and victimization. Our findings substantiate the notion that higher exposure to fraudulent activity significantly increases the likelihood of victimization. The results also show that vigilance negatively moderates the relationship between fraud exposure and fraud victimization, suggesting that individuals with higher vigilance are better at identifying scams, thereby decreasing their likelihood of becoming fraud victims. Furthermore, financial literacy is positively related to vigilance, indicating that financially literate individuals are more aware of potential scams. However, the predictive power of financial literacy on vigilance is relatively low. Hence, while literacy helps a person sharpen their indicators for detecting fraud, psychological, behavioral, and contextual factors may also affect their vigilance and decision-making. Full article

Background: The identification of pathogenic variants in the Breast Cancer Genes 1 and 2 (BRCA1/2) is a critical predictive biomarker for poly (ADP-ribose) polymerase inhibitor (PARPi) therapy in epithelial ovarian cancer (EOC). The aim of this study is to define real-world rates and determinants of germline and somatic BRCA1/2 testing and subsequent PARPi utilisation in Australia using a national clinical quality registry. Methods: This multi-centre cohort study analysed data from 1503 women with non-mucinous EOC diagnosed between May 2017 and July 2022, captured by the Australian National Gynae-Oncology Registry (NGOR). We evaluated rates of germline and somatic testing and PARPi use, using multivariate logistic regression to identify associated clinical and demographic factors. Results: Overall germline and somatic testing rates were 68% and 32%, respectively. For the high-grade serous ovarian cancer (HGSOC) cohort, rates were higher, at 78% and 39%, respectively. Germline testing was significantly less likely for women aged >80 years (OR 0.49), those in regional areas (OR 0.61), and those receiving single-modality treatment. Somatic testing uptake increased significantly following public reimbursement for PARPi (p = 0.004). Among eligible women with a newly diagnosed BRCA pathogenic variant and advanced disease (n = 110), 52% commenced first-line maintenance PARPi. Conclusions: This national study offers valuable insights into Australian ovarian cancer care, highlighting opportunities to enhance testing equity for older women (aged >80) and regional patients. Furthermore, it identifies the translation of a positive test into PARPi therapy as a complex area that warrants further collaborative investigation to optimise patient outcomes. Full article

Background/Objectives: This retrospective multicenter study, conducted within the ORCHESTRA Project, investigated SARS-CoV-2 reinfections among 5777 healthcare workers (HCWs) from four University Hospitals (Modena, Verona, Padova and Trieste) in northern Italy, aiming to assess the risk of reinfection and its determinants, comparing the clinical characteristics of reinfections with those of first infections, and examining the impact of preventive measures and vaccination strategies. Methods: HCWs completed an online questionnaire between June and August 2022. The survey collected demographic, occupational, and clinical data, including information on first infections and reinfections. Statistical analyses were performed using SPSS 28.0, through bivariate and multivariate approaches. Results: Response rates were 41.8% for Modena, 39.5% for Verona, 17.9% for Padova, and 17.4% for Trieste. Among the respondents, 4.8% (n = 276) experienced 2 infections and 0.5% (n = 27) reported 3 infections, out of a total of 330 reinfection cases. Additionally, 43.0% (n = 2787) reported only one infection, while 51.5% were never infected. Reinfection rates increased across five study phases (based on the epidemiological context), likely due to the emergence of new SARS-CoV-2 variants. A booster vaccine dose significantly reduced reinfection risk. Higher reinfection risk was found among HCWs aged ≤30 years, those with chronic respiratory diseases, and those working in COVID-19 wards, particularly nurses and allied health professionals. Reinfections were associated with a lower frequency of symptoms both during the period of swab positivity and after a negative swab, as well as with a shorter duration of swab positivity. No significant differences in symptom duration were found between first infections and reinfections. Conclusions: Despite its limitations, the online questionnaire proved a useful tool. Natural infection and vaccination reduced both reinfection risk and symptom severity. Prior infections should be considered in planning vaccination schedules and prioritizing HCWs. Full article

Objectives: The aim of this study was to analyse no-show patterns in healthcare appointments, identify associated factors, and explore key determinants influencing non-attendance. Study Design: This was a retrospective observational study. Methods: We analysed 120,405 healthcare appointments from 2022–2023 in Turin, Northern Italy. Data included demographics, appointment characteristics, and attendance records. Logistic regression identified significant predictors of no-shows, adjusting for confounders. Results: A 5.1% (n = 6198) no-show percentage was observed. Younger patients (<18 years) and adults (18–65 years) had significantly higher odds of missing appointments than elderly patients (>65 years) (OR = 2.32, 95% CI: 2.17–2.47; OR = 2.46, 95% CI: 2.20–2.74; p < 0.001). First-time visits had a higher no-show risk compared to follow-up visits and diagnostics (OR = 1.11, 95% CI: 1.04–1.18; p < 0.001). Each additional day of waiting increased the likelihood of no-show by 1% (OR = 1.01, 95% CI: 1.01–1.01; p < 0.001). Conclusions: No-show percentages are influenced by demographic and service-related factors. Strategies targeting younger patients, longer waiting times, and non-urgent appointments could reduce no-show percentages. Full article

Background/Objectives: Access to health services is often limited due to socio-economic and organizational determinants of health systems, which lead to increased unmet healthcare needs. This study aimed to identify access barriers for the general population in Greece, including those that may have emerged following the COVID-19 pandemic. Methods: This was a cross-sectional survey of 1002 Greek citizens. A questionnaire regarding socio-demographics, healthcare utilization, and access to health services was used. Interviews took place between October and November 2022. Results: Of 837 participants who used health services in 2022, 82.6% had a medical consultation, 80.6% took diagnostic tests, and 63.6% visited a pharmacy for pharmaceuticals. Of those having a medical consultation, 33.1% did so at an NHS health unit, while 75% of the participants taking diagnostic tests visited a contracted private laboratory. Out of the 135 participants requiring hospitalization, 62% were hospitalized in a public hospital, while 85% of the participants requiring pharmaceuticals visited a private pharmacy. Access barriers in the past year were reported by 48% of the participants requiring a medical consultation, 34% of the participants requiring diagnostic tests, and 40% of the participants requiring hospitalization. The most common barriers were long waiting times and financial constraints. The main barrier to accessing pharmaceuticals was the availability and administration of the product. Conclusions: The identified healthcare access barriers highlight the vulnerabilities of the current health system in Greece, which were further exposed during the COVID-19 pandemic crisis. Addressing socioeconomic factors that are considered key access indicators should be the focus of future health policy initiatives. Full article

Background/Objectives: The SARS-CoV-2 and influenza A (H1N1)pdm09 pandemics have resulted in high numbers of ICU admissions, with high mortality. Identifying risk factors for ICU mortality at the time of admission can help optimize clinical decision making. However, the risk factors identified may differ, depending on the type of analysis used. Our aim is to compare the risk factors and performance of a linear model (multivariable logistic regression, GLM) with a non-linear model (random forest, RF) in a large national cohort. Methods: A retrospective analysis was performed on a multicenter database including 8902 critically ill patients with influenza A (H1N1)pdm09 or COVID-19 admitted to 184 Spanish ICUs. Demographic, clinical, laboratory, and microbiological data from the first 24 h were used. Prediction models were built using GLM and RF. The performance of the GLM was evaluated by area under the ROC curve (AUC), precision, sensitivity, and specificity, while the RF by out-of-bag (OOB) error and accuracy. In addition, in the RF, the im-portance of the variables in terms of accuracy reduction (AR) and Gini index reduction (GI) was determined. Results: Overall mortality in the ICU was 25.8%. Model performance was similar, with AUC = 76% for GLM, and AUC = 75.6% for RF. GLM identified 17 independent risk factors, while RF identified 19 for AR and 23 for GI. Thirteen variables were found to be important in both models. Laboratory variables such as procalcitonin, white blood cells, lactate, or D-dimer levels were not significant in GLM but were significant in RF. On the contrary, acute kidney injury and the presence of Acinetobacter spp. were important variables in the GLM but not in the RF. Conclusions: Although the performance of linear and non-linear models was similar, different risk factors were determined, depending on the model used. This alerts clinicians to the limitations and usefulness of studies limited to a single type of model. Full article

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder with significant racial and ethnic disparities in prevalence, disease severity, and outcomes. Cardiovascular complications, including pericarditis, myocarditis, valvular disease, and conduction abnormalities, contribute to increased morbidity and mortality in SLE patients. This study examines racial and ethnic disparities in cardiovascular outcomes among hospitalized SLE patients in the United States. Methods: This retrospective study utilized the National Inpatient Sample (NIS) database from 2016 to 2021 to analyze hospitalizations of adult patients (≥18 years) with a primary or secondary diagnosis of SLE. Patients were stratified into racial/ethnic groups: White, Black, Hispanic, Asian, Native American, and Other. Primary outcomes include major adverse cardiovascular events (MACEs), which are a composite of in-hospital mortality, myocardial infarction (MI), sudden cardiac death, and other SLE-related outcomes including cardiac, pulmonary, and renal involvement. Statistical analyses included multivariable logistic regression models adjusted for demographic, socioeconomic, and hospital-related factors to assess racial disparities. Results: The study included 514,750 White, 321,395 Black, and 146,600 Hispanic patients, with smaller proportions of Asian, Native American, and Other racial groups. Black patients had significantly higher odds of in-hospital mortality (OR = 1.17, 95% CI = 1.08–1.26, p < 0.001) and sudden cardiac death (OR = 1.64, 95% CI = 1.46–1.85, p < 0.001) compared to White patients. Asian patients also exhibited increased mortality risk (OR = 1.37, 95% CI = 1.14–1.63, p = 0.001) as compared to Whites. Conversely, Black (OR = 0.90, 95% CI = 0.85–0.96, p = 0.01) and Hispanic (OR = 0.87, 95% CI = 0.80–0.96, p = 0.03) patients had lower odds of MI. Racial disparities in access to care, socioeconomic status, and comorbidity burden may contribute to these differences. Conclusion: Significant racial and ethnic disparities exist in cardiovascular outcomes among hospitalized SLE patients. Black and Asian individuals face higher in-hospital all-causes mortality and sudden cardiac death risks, while Black and Hispanic patients exhibit lower MI rates. Addressing social determinants of health, improving access to specialized care, and implementing targeted interventions may reduce disparities and improve outcomes in minority populations with SLE. Full article