European Journal of Public Health最新文献

Understanding local level impact of socio-economic and spatial disparities on health outcomes is crucial for informing effective public health interventions. This study examines the association between socio-economic factors, centrality, and premature mortality-measured as years of life lost (YLLs)-across Norwegian municipalities. We conducted an ecological, cross-sectional registry-based study across municipalities and districts, each with populations exceeding 1000 as of 1 January 2019. Data on mortality, demographics, education, income, and centrality were sourced from Statistics Norway. All-cause YLLs were calculated by multiplying age-specific mortality numbers by aspirational life expectancy from the Global Burden of Disease 2019 life tables. Municipalities were divided into quartiles based on a composite socio-economic position (SEP) score that integrated education and income, and grouped into centrality categories. Mixed-effects negative binomial regression models, crude and adjusted for age categories and sex, evaluated both relative and absolute associations. The lowest SEP quartile, assessed with a composite SEP score, had a 15% higher YLL rate compared to the highest quartile [incidence rate ratio (IRR) = 1.15; 95% confidence interval (CI) = 1.07-1.24], amounting to an absolute difference of 2127 YLLs per 100 000 population. Similarly, the least central quartile exhibited a 15% higher YLL rate compared to the most central one (IRR = 1.15; 95% CI = 1.09-1.21), translating to an absolute difference of 2057 YLLs per 100 000 population. There are substantial inequalities in premature mortality across Norwegian municipalities, strongly linked to municipal SEP and centrality.

Every year, a significant number of people in Finland retire on a disability pension. Half of them are granted a temporary disability pension. Our study examines the different pathways into and out of temporary disability retirement and analyses how individual-level factors are associated with these pathways. We used comprehensive register data on Finns aged 30-58 who received a temporary disability pension in 2018 and followed them for 4 years before and after their pension claim. Sequence analysis in combination with clustering was used to identify typical patterns in and out of temporary disability pension. In addition, we examined the association between individual-level factors and the observed pathways. We identified nine distinct clusters with unique pathways. Two-thirds of the study population belonged to five clusters in which receipt of a disability pension continued for several years. The majority had been employed before receiving the pension. Only 18% belonged to a single cluster characterized by return to work, while persons in the remaining three clusters (20%) ended up in unemployment or unknown labour market states. The different pathways also differed by individual-level factors. We identified several different pathways into and out of temporary disability pensions. Most of them were characterized by long-term disability retirement rather than return to work. A better understanding of these pathways and associated individual factors is crucial to developing more effective strategies to facilitate return to work and prevent long-term disability.

Alcohol use and alcohol use disorder (AUD) are major contributors to global morbidity and mortality. The Alcohol Use Disorders Identification Test (AUDIT) is often used for screening of alcohol use and potential alcohol problems, but less is known whether AUDIT can predict a diagnosis of AUD. Responses to the AUDIT questionnaire (N = 29 278) from two waves of a population health survey (The Tromsø study) were used to predict a diagnosis of AUD in national health registries over the following three years. Covariates included age, sex, educational level, family income, and mental health score. Overall, 13%-15% scored above the lowest level on AUDIT, with slightly higher figures in males and younger adults, among those with higher education, or with higher mental distress. Few were represented in national health registries (2.1% and 2.7% of these cases in the primary and specialist healthcare, respectively), but with higher figures among those with the highest AUDIT scores. Being female, of older age, having a lower income, and reporting more mental health symptoms increased the probability of receiving an AUD diagnosis. Younger age, male gender, higher education, and higher mental health score predicted higher AUDIT scores, but few, even with high AUDIT scores, were represented in national health registries with an AUD diagnosis. Furthermore, with a high AUDIT score, factors such as older age, lower income, and lower education increased the likelihood of receiving an AUD diagnosis. This suggests that relying on national health registries to monitor alcohol morbidity may be challenging.

Few studies have examined the implications of the Covid-19 pandemic on mental health across different employment branches. This study investigated the impact of the pandemic on long-term sickness absence (SA) with common mental disorders (CMDs) and antidepressant prescriptions in different employment branches and age groups in Sweden. Using national registers, we observed the Swedish population (18-65 years) with gainful employment quarterly from 2018 to 2021. An interrupted time-series design was employed to examine changes in trends of incidence rates (IRs) for (i) long-term (>90 days) SA with CMDs and for (ii) antidepressant prescriptions across eight employment branches during versus pre-pandemic. Analyses were stratified by age group. There was no evidence of outcome changes in the entire working age population. However, compared to pre-pandemic levels, the IRs of long-term SA with CMD increased by 5.9% per quarter for those working in the cultural sector [95% confidence interval (CI): 2.2%-9.8%], 3.4% in trade and transportation (95% CI: 0.4%-6.4%), and 5.5% in manufacturing and services (95% CI: 1.5%-9.7%) as well as among individuals aged 56-64. Incident antidepressant prescription rates were marginally higher for workers in construction (1.1% annual increase; 95% CI: 0.1%-2.1%), culture (1.4%; 0.7%-2.0%), and trade and transportation (0.9%; 0.1%-1.7%). While the risk of CMD-related long-term SA or incident antidepressant prescription in Swedish workers did not appear to be impacted by the pandemic, certain employment branches and older individuals were negatively affected in terms of both outcomes. Targeted countermeasures and initiatives to improve well-being are necessary for vulnerable groups.

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.