European Journal of Public Health最新文献

Several diagnoses have been associated with increased risk for adverse health outcomes following COVID-19 infection. Whether these diagnoses also predispose individuals to long-term sickness absence (SA) due to COVID-19 is unclear. The aim was to examine associations between risk diagnoses and long-term SA due to COVID-19/COVID-19-like diagnoses among blue-collar workers in the retail and wholesale industry. We conducted a two-year prospective cohort study of all blue-collar workers aged 18-67 in 2019, employed in retail and wholesale industry in Sweden (N = 292 274), using linked microdata from several nationwide registers. We estimated odds ratios (OR) through logistic regression models to determine the association between diagnoses linked with adverse health outcomes following COVID-19 infection and SA spells due to COVID-19/COVID-19-like diagnoses in 2020-2021. We included COVID-19-like diagnoses because of diagnostic uncertainty early in the pandemic. Of all the workers, 34 594 (11.8%) had least one COVID-19 risk diagnosis. Only 7812 (2.7%) workers had SA due to COVID-19/COVID-19-like diagnoses in the two-year follow-up (2020-2021). Most risk diagnoses were associated with elevated likelihoods of SA due to COVID-19, particularly immunodeficiency (OR 2.58; 95% CI 1.68-3.27) and respiratory disease (2.00; 1.89-2.12). The associations for these diagnoses and diabetes and hypertension persisted after we stratified by prior SA, but no other risk diagnosis was significantly associated with SA due to COVID-19/COVID-19-like diagnoses among those with prior SA in 2019. Stratifying on all-cause SA thus removed the association between most COVID-19 risk diagnoses and SA due to COVID-19/COVID-19-like diagnoses.

Delays in breast cancer (BC) diagnosis and treatment negatively impact survival outcomes. Understanding patient- and provider-related factors behind these delays is crucial. This study aimed to identify nationwide reasons for delayed diagnosis and treatment of locally advanced BC in Turkiye. A prospective, multicenter hospital-based survey was conducted across 35 institutions between 2023 and 2024. Patient- and provider-related delays were assessed via a structured 61-item face-to-face survey, supplemented by clinical data from electronic health records. Delays exceeding 3 months were clinically categorized as significant. A total of 1322 women participated from seven regions across Turkiye. Factors contributing to diagnostic delays on a national level included economic reasons (5.5%), lack of family support (3.3%), lack of knowledge (12.4%), lack of time due to household work (3.8%), difficulty in finding an appointment (6.7%), pregnancy-related reasons (1.1%), fear of losing the breast (8.9%), fear of death (9.8%), and transportation difficulties (5.1%). Provider-related delays were infrequent. About 89.3% of the patients had the initial doctor appointment and 89.6% had the first specialist consultation within one month. Treatment planning was predominantly based on a multidisciplinary team decision in 88.3% of patients. Regarding treatment initiation, 93.2% started required treatment within 1 month of decision. Patient-related factors are the major causes of diagnostic delay in Turkiye. On the other hand, from the provider's perspective, the presence of multidisciplinary teams, including dedicated breast surgeons, represents a key factor in ensuring the timely implementation of diagnostic procedures and treatment strategies.

This article investigates predictive associations between diabetes and depressive symptoms across Ireland, the United Kingdom, and four European regions. The data were obtained by merging datasets from three large prospective cohort studies-the English Longitudinal Study on Ageing, The Irish Longitudinal study on Ageing, and the Survey on Health, Ageing and Retirement in Europe. We first applied a survival analysis design to two samples of 43 061 and 35 993 participants, investigating elevated depressive symptoms as a risk factor for diabetes, and diabetes as a risk factor for elevated depressive symptoms, respectively. We next applied a multilevel modeling approach to examine depressive symptoms before, during, and after diabetes onset across 101 799 participants. We found a bidirectional association between diabetes and depressive symptoms; however, the strength of these associations did not significantly differ between the regions (P > .01). The results also showed that individuals with newly diagnosed diabetes consistently reported higher depressive symptoms than those without diabetes, even before diagnosis. However, we observed no country-specific differences in the gradual changes in depressive symptoms regardless of participants' diabetes status. Diabetes at baseline was associated with higher risk of developing depression; and vice versa. These associations were not moderated by geographical location. Therefore, the risks of diabetes and depressive symptoms comorbidity seem to be equal across all observed geographic regions.

Area-level measures of deprivation in Sweden often rely on limited socio-economic indicators, such as income or education. To address this, we developed the Index of Multiple Deprivation in Sweden (IMDIS) to capture a multitude of explanatory factors for socio-economic inequalities and the distribution across small areas in Sweden. The IMDIS is a compositional index constructed for small areas in Sweden in 2015 and combines 15 indicators across 4 domains (Housing, Employment, Income and Capital, and Education) into an overall deprivation score. Indicators were selected and spatially smoothed to mitigate the effect of small numbers and increase robustness. Domains were constructed using a weighted average of underlying indicators, allowing detailed examination of the significance each domain or indicator has in small areas, and were further combined using explicit weights. All areas were subsequently ranked from the 1st least to 5984th most deprived area. For each area, we generated three key outputs: a score, a rank, and assignment to a deprivation decile. The IMDIS showed high internal consistency and revealed stark geographic inequalities in deprivation. The most deprived areas were concentrated in urban regions, particularly Stockholm, Gothenburg, and Malmö. Housing deprivation was more prominent in urban areas, while educational deprivation was more prevalent in rural and peripheral regions. The IMDIS offers a comprehensive measure of multiple deprivation at the small-area level in Sweden. Its domains and indicators can be used individually or combined to identify inequalities in vulnerable areas and explore geographic patterns, supporting a deeper understanding of social disparities.

Despite many challenges, Ukraine has prioritized the need to develop and professionalize the public health workforce (PHWf) to respond to its transitioning public health (PH) system. This study explores how various PH stakeholders can develop and advance the PHWf professionalization programme in Ukraine using the WHO-ASPHER Roadmap to Professionalize the PHWf in the European region as the theoretical foundation. A mixed-methods qualitative approach was used to (i) document analysis of Ukrainian policy and regulatory documents related to PHWf and (ii) qualitative survey research involving relevant PH stakeholders. Directed and conventional content analysis was applied to analyse data. The document analysis reviews key areas as LoPHS, PH higher education, and continuous professional development. The analysis helped explain the current state of the PHWf and to understand enabling factors for its professionalization. Results from the qualitative survey research suggest several actions to enhance PHS in Ukraine based on three priority areas: (i) PH laws and regulation; (ii) PH education and training; and (iii) financing for the development and professionalization of PHWf. The PHWf professionalization programme for Ukraine resonates with the WHO-ASPHER Roadmap to Professionalizing the PHWf in the European region. It attempts to close the gap between PH legislation, the current state of the PHWf, and international PH practice ambitions. The study suggests the need for a well-coordinated development process across PHS and PHWf domains.

The growth of scientific literature on large language models (LLMs), such as ChatGPT, anticipates their central role for accessing health information but poses potential risks, including the false belief that artificial intelligence (AI) could replace doctors in providing reliable information. Our study, part of the Slow AI project launched in partnership with the Slow Medicine ETS Association, reviewed the literature on ChatGPT use by the public, analyzing citizens' and patients' perceptions of using AI for health-related questions, identifying key benefits and concerns, and providing recommendations for the safe and effective use of LLMs. We conducted a narrative review following PRISMA guidelines, including qualitative, quantitative, and mixed-methods studies, selected through a search of the PubMed database. Data were extracted and analyzed using a predefined form. Out of 388 records, 120 studies were included, primarily from the USA (65), Europe (19), and Asia (15). Most studies focused on general medicine (37), with patients (57) being the main participants. Key findings include that LLMs improve access to health information, aiding diagnostic accuracy and patient understanding. However, risks exist, such as inaccurate or outdated information, lack of empathy, and privacy concerns. These challenges highlight the need for reliable AI training with real-world data and clinician oversight to mitigate risks. Lastly, while LLMs can improve communication, they should complement, not replace human interaction. LLMs in healthcare offer great potential but also present risks. Safeguards and clinician oversight are crucial to preserve patient safety and doctor-patient relationship.

Endovascular therapy (EVT) is standard care for acute ischemic stroke due to large vessel occlusion, but its availability is limited in areas with long distances. It has also been demonstrated that there are differences in the utilization of thrombectomy related to socioeconomic factors. The aim of this study is to examine regional differences in the utilization of mechanical thrombectomy and outcome within one comprehensive stroke center district in terms of distance and income. This retrospective single-center study included 352 patients with mechanical thrombectomy in Oulu University Hospital catchment area between the years 2015 and 2019. Socioeconomic status was determined according to the income of residential area. Age-adjusted rate was significantly higher in the highest income areas compared to the lowest and middle third areas; 52.29 (95% CI, 42.85-61.72)/100 000 vs. 34.33 (95% CI, 28.14-40.52) vs. 38.03 (95% CI, 31.14-44.92)/100 000 inhabitants/year. The corresponding rates in rural areas were: 73.37 (95% CI, 53.23-93.51) vs. 37.11 (95% CI, 28.66-45.57) vs. 45.44 (95% CI, 34.72-56.16)/100 000 inhabitants/year. In this study, we found significant differences in the utilization of the EVT within one comprehensive stroke center district. These differences are explained by the income and the rurality of the residential area.

Social inequalities in alcohol-related morbidity and mortality are well-established, but the reasons are not fully understood. One possible reason is labor market difficulties stemming from lower educational qualifications, leading to alcohol-related harm. The present study aims to investigate the extent to which differences in labour market marginalization (LMM) (including differences in timing and type of LMM) explain educational differences in alcohol-related mortality, and whether this is independent of pre-labor market selection factors. A register-based cohort study included all men born between 1949 and 1951 who underwent Swedish military conscription in 1969/70 and were alive at age 55 (n = 45 168). Nationwide registers provided data on education level and alcohol-related mortality. LMM was measured by unemployment, sickness absence, and disability pension. Pre-labor market factors included health behaviors, cognitive ability, and health from conscription exams. Cox regression analyses were used to obtain hazard ratios (HR) with 95% confidence intervals (CI). The explanatory role of LMM was assessed by percentage attenuation of HR. Men with primary and secondary education had higher risks of alcohol-related mortality (HR: 4.23, HR: 2.92) compared to those with university education. LMM explained a substantial part of these differences (42% and 37%). However, LMM's effect was smaller (18% and 7%) when pre-labor market factors were considered. Men with lower education levels in Sweden are more likely to die from alcohol-related causes compared to higher educated men. While differences in LMM contribute to these disparities, its explanatory power diminishes when considering pre-labor market factors, suggesting potential selection effects.