Patient information leaflets (PILs) for medicines are legally required across the European Union to support safe medicine use, informed decision-making, and treatment adherence. However, they are often criticised for complexity, poor readability, and anxiety-inducing descriptions of side effects. Health literacy (HL), the ability to access, understand, appraise, and apply health information, may affect how patients engage with PILs. Italy has a high prevalence of limited HL, yet population-based evidence on its influence remains scarce. This study examined patterns of PIL use, comprehension, and related behaviours in the Italian adult population, accounting for HL differences. A cross-sectional survey was conducted between October 2024 and April 2025 among 1,977 adults (mean age 46.5 years; 63% women). Recruitment combined social media dissemination with general practitioner collaboration to reach older and less digitally active individuals. The questionnaire collected sociodemographic data, HL (using the six-item HLS-EU-Q6 and a single-item functional HL screener), and indicators of PIL engagement, including reading frequency, reasons for non-consultation, comprehension of extracts, anxiety, therapy discontinuation, risk interpretation, and perceptions of commercial influence. Data were analysed using descriptive and inferential statistics. 66% of respondents reported always reading PILs for prescription medicines, and 71% did so for over-the-counter products. Overall, 17.2% had inadequate HL, 52.4% problematic, and 30.4% sufficient. Among sociodemographic variables, only education differed by HL level (2.09 vs. 2.02; p = 0.04). Higher HL was associated with more frequent PIL reading, greater confidence in medicine use (mean 2.22 vs. 1.94; p < 0.001), and lower anxiety (2.21 vs. 1.97; p < 0.001). In contrast, respondents with lower HL more often discontinued therapies after reading PILs (37% vs. 28%; p = 0.02) and overestimated vaccine risks compared with analgesics (25% vs. 9%). Perceiving PILs as commercially influenced was also more common among those with limited HL (p < 0.001). HL significantly shapes how Italian adults use and interpret PILs, influencing engagement, confidence, anxiety, and therapy behaviours. Current formats may inadvertently widen inequalities by overburdening readers with limited HL. Public health efforts should prioritise simplification, plain language, balanced risk communication, and perceptions of neutrality to promote equitable and safe medicine use.

BackgroundLong COVID affects over 65 million people worldwide, yet the impact of social determinants of health (SDoH), such as socioeconomic status, race/ethnicity, education, occupation, and geography, remains poorly understood. To evaluate the association between SDoH and the risk and severity of Long COVID.MethodsA rapid review of observational studies was conducted using MEDLINE, Embase, and Web of Science (up to September 29, 2024). Studies reporting original data on SDoH and Long COVID outcomes were included. Data were extracted on study characteristics, population demographics, Long COVID definitions, and SDoH-related findings. Study quality was assessed using the Newcastle-Ottawa Scale.ResultsSeventy-one studies (43 cohort, 28 cross-sectional) were included. Definitions of Long COVID varied. Commonly studied SDoH included age, sex, race/ethnicity, education, financial security, employment, and geography. Female sex and older age were consistently associated with increased risk and severity of Long COVID. Black and Hispanic individuals were more likely to experience Long COVID. Lower education and financial insecurity were also linked to greater prevalence and symptom burden. Frontline and essential workers were found to be at increased risk. Geographic disparities were evident but varied across rural and urban residence.ConclusionsSDoH play a key role in shaping Long COVID outcomes. Addressing these disparities requires targeted public health efforts and standardized case definitions.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13690-025-01787-x.

BackgroundLeading global causes of death are noncommunicable diseases (NCDs), which affect mostly low- and middle-income countries. Over 90% of all NCD deaths occur in China, where multimorbidity, which is associated with adverse social circumstances, is an emerging health priority. Rates in China are significantly higher than the rest of the world. The purpose of this study was to investigate, using structural equation modeling (SEM), the relationships and pathways between lifestyle and socioeconomic factors and the prevalence of chronic noncommunicable disease (NCD) multimorbidity in rural Southwest China, an area with limited resources, a high degree of ethnic diversity, and an increasing prevalence of multimorbidity.MethodsData were collected from a cross-sectional health interview and examination survey among individuals aged ≥ 35 years in rural Southwest China. A standard questionnaire was used to gather data on the demographic traits and lifestyle choices of the participants. Each participant’s height, weight, waist circumference (WC), blood pressure, fasting blood glucose, and spirometry before and after bronchodilators were assessed. Principal component analysis was used to create a single socioeconomic position (SEP) index.ResultsThe overall prevalence rates of six common chronic NCDs—raised blood pressure, raised blood glucose, hyperlipidemia, coronary heart disease (CHD), stroke, and chronic obstructive pulmonary disease (COPD)—and multiple comorbidities were 56.3%, 10.8%, 7.7%, 6.0%, 4.2%, 3.8%, and 18.9%, respectively. Males had a greater prevalence of COPD than females did, whereas females had greater prevalences of raised blood pressure and hyperlipidemia than males did (p < 0.01). The SEM results indicated that SEP, age, ethnicity, physical inactivity, and an unbalanced diet had both significant direct and indirect effects on NCD multimorbidity, with total effect sizes quantified at -0.065, 0.321, -0.057, 0.110, and 0.090, respectively (p < 0.05). the condition of obesity ( 0.401, p < 0.01) and a familial predisposition to NCDs (0.134, p < 0.01) were directly associated with multiple morbidities.ConclusionsBoth lifestyle and socioeconomic factors are associated with the prevalence of NCD multimorbidity in rural Southwest China. Future interventions for the prevention and control of NCD multimorbidity should focus more on individuals with a low SEP and unhealthy lifestyles.

IntroductionThis scoping review investigates the landscape of patient navigation programs (PNP) specifically for lung cancer, addressing a gap in existing literature that has predominantly focused on other cancers or chronic diseases and screening.MethodsEmploying Arksey and O’Malley’s framework and PRISMA-ScR guidelines, we aimed to characterize the features of lung cancer PNP, understand their outcomes, and analyse the applied methodologies for research. The search was conducted in January 2025.ResultsThe search led to the inclusion of 16 studies, predominantly from high-income countries, revealing a variety of implementation strategies and navigator types. Findings indicate a significant emphasis on administrative outcomes, clinical and patient-reported outcomes with heterogeneity in its use. Emotional support, logistical assistance, and healthcare system navigation emerged as key themes in service delivery, however PNP focus is inconsistent. The analysis highlighted the need for standardized methodologies and outcome measures, as well as greater inclusivity in research populations. Importantly, the review identified a trend of excluding marginalized demographics, raising concerns about the generalizability and external validity of findings.ConclusionOur study underscores the necessity for future research to harmonize PNP methodologies, enhance transparency in collaborative efforts, and focus on addressing health inequalities to optimize the effectiveness of lung cancer navigation initiatives.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13690-025-01786-y.

BackgroundProlonged patient waiting times in healthcare settings, particularly within humanitarian contexts, pose significant challenges to the provision and quality of care received. This study examines the persistent issue of extended waiting periods among patients, including Syrian and Palestinian refugees and Lebanese nationals seeking care at humanitarian clinics in Lebanon. The study aims to highlight the challenges faced by these populations and their impact on care and health outcomes.MethodsThis study analyzed secondary data for 111,998 patient records receiving care at Multi Aid Programs clinic between December 2018 and June 2023. Descriptive statistics were performed on the compiled data. Additionally, an Auto-Regressive Integrated Moving Average (ARIMA) model was developed to forecast patient waiting times over the next 14 days, providing a data-driven approach to understanding and optimizing healthcare service delivery.ResultsA total of 442,332 patient visits were analyzed. The vast majority of patients treated at the clinic were Syrian refugees (84.6%), Lebanese patients (15.3%), and Palestinian refugees(0.1%). Females accounted for 55% of visits, slightly outnumbering males (45%). Approximately 21.4% of patients left the clinic without receiving medical care, likely due to extended waiting times. The average waiting time was 1.4 h with an average of a 1-hour wait for emergencies and a 2-hour wait for orthopedic appointments. The study team, along with clinic staff and administration, observed patients waiting in overcrowded rooms. Little improvement has been reported in waiting times since 2019. The predictive ARIMA model for patient waiting time forecasting yielded a notably low RMSE of 0.18, indicating excellent fidelity between the modeled and true wait times.ConclusionPatient waiting time represents a significant issue in the humanitarian setting, jeopardizing patient access to medical care and often resulting in patients leaving clinics before being seen by a physician. Future research should explore the underlying causes and broader consequences of prolonged wait times, including their impact on patient satisfaction, healthcare provider burnout, and overall system efficiency.

BackgroundMultimorbidity is highly prevalent among individuals with diabetes and exerts a substantial impact on healthcare systems. This study aims to investigate the prevalence and healthcare costs of multimorbidity in patients with type 2 diabetes and to assess the influence of multimorbidity on healthcare expenditures using machine learning approaches.MethodsWe conducted a retrospective cohort study utilizing chronic disease management database and health insurance claim database from a city in eastern China. Twenty-nine multimorbidities with a prevalence exceeding 1% among diabetic patients were identified using ICD codes. We analyzed the trends in prevalence and healthcare costs from 2014 to 2019. Machine learning models were developed to predict healthcare expenditures, and SHAP analysis was applied to the optimal model to evaluate the contribution of specific multimorbidity to healthcare costs.ResultsAmong 79,910 patients, the prevalence of multimorbidity increased from 87.9% in 2014 to 99.3% in 2019, while the proportion of healthcare costs attributed to multimorbidity rose from 31.8% to 34.2%. In 2019, the most prevalent conditions were hypertension (88.3%), arthritis (74.7%), and chronic ischemic heart disease (54.6%), whereas the highest-cost conditions included sequelae of cerebrovascular disease ($3,860.8), cerebral infarction ($2,768.8), and renal failure ($1,543.9). SHAP analysis revealed that cerebrovascular disease sequelae, heart failure, chronic ischemic heart disease, and chronic obstructive pulmonary disease had the most significant impact on future healthcare costs for diabetic patients.ConclusionsMultimorbidity is nearly universal among individuals with diabetes in China, with cardiovascular, cerebrovascular, and chronic respiratory diseases contributing disproportionately to healthcare expenditures.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13690-025-01746-6.

BackgroundHigh-quality immunization data is critical for making evidence-based decisions, ensuring effective program implementation, and achieving intended outcomes. Conversely, low-quality data hinders program monitoring and evaluation activities. Despite efforts to improve data quality in the health sector, the status of immunization data quality in Ethiopia is not well-known. This national study examined immunization data quality status and its challenges in Ethiopia.MethodsA mixed-method study was conducted on health facilities and all levels of administrative units, including the Ministry of Health, 12 regional health bureaus, 51 zonal health departments, 233 district health offices, 395 health facilities, and 267 health posts from April 15 to July 30, 2023. In-depth interviews were conducted with 74 purposefully selected key informants across these levels. The quality of immunization data was assessed based on accuracy, timeliness, and completeness. Open Data Kit (ODK) was used to collect quantitative data, and STATA statistical software was used for analysis. Qualitative data were analyzed using the thematic analysis approach with ATLAS.ti.ResultsOut of 4062 immunization data elements expected to be reported in the months under review, only 2413 (59.4%) were available. One-third of health facilities had acceptable reporting accuracy, while 27% had inaccurate reports. 41% of health facilities had inadequate documentation for verifications. Complete documentation and accuracy of reporting were better in hospitals than in health centers and health posts, but lower in pastoralist settings and some regions. Report timeliness ranged from 94% in the Oromia region to 37% in Gambella. The main reasons for low immunization data quality were inadequate skilled data management personnel, lack of standard data management and data quality assessment tools, and irregular data quality assessments at health facilities.ConclusionsThe study identified a significant immunization data quality problem in Ethiopia’s health institutions, with data accuracy, timeliness and completeness rates falling far below the standard. To improve immunization data quality and informed decision-making, it is crucial to strengthen data quality improvement strategies starting from the point of data collection.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13690-025-01758-2.

BackgroundThe rapid urbanization and demographic shifts experienced globally have significant implications for the well-being of older adults, particularly in low and middle income countries like Nigeria. As individuals age, understanding the impact of environmental characteristics on mobility and mental well-being becomes crucial for optimal health. Walkable neighborhoods have the potential to enhance mobility, social engagement, and well-being among older adults. This study explored the neighborhood characteristics and its relationship with mobility and mental well-being of older adults in Nigeria. It also identified the policy implications of the findings for urban planning and public health interventions.MethodsThis cross-sectional study involved 162 older adults residing in Enugu urban area between August 2023 and April 2024. The Neighborhood Environment Walkability Scale, Life Space Questionnaire and Warwick-Edinburgh Mental Well-being Scale assessed the neighborhood characteristics, mobility and mental well-being respectively. Correlation and multiple regression analysis were employed.ResultsSignificant relationships were observed between mobility and residential density (r= -0.223, p = 0.004), connectivity (r = 0.266, p = 0.001) aesthetics (r = 0.212, p = 0.007), and safety from traffic (r = 0.214, p = 0.006). The regression analysis with mobility as the dependent variable showed a good model fit and was significant [F = 7.389, p < 0.001, adjusted R2 = 0.28, R2 = 0.33]. The predictors/independent variables in the model accounted for 33% of the variance in mobility. Durbin Watson’s score was 2.066 indicating independence of observations in the model. Variance Inflation factor values were less than 10 while Tolerance values were greater than 0.20 indicating no multicollinearity in the model. Key predictors of mobility were residential density, land use mix, connectivity, aesthetics, safety from traffic, environmental description and child safety. The stepwise regression identified five significant predictors: connectivity, residential density, aesthetics, child safety, and traffic safety. These explained 27.5% of the variance in mobility scores (R² = 0.275, p < 0.001). While explaining slightly less variance than the full model, it offered a more interpretable structure. Interestingly, there was no significant relationship between neighborhood characteristics and mental well-being (p > 0.05), and none of these characteristics emerged as significant predictors of mental well-being.ConclusionsUrban planners, policymakers, and healthcare professionals should collaborate to create age-friendly environments that prioritize mobility and well-being for older adults. The insights from this study can inform policy recommendations and guidelines aimed at enhancing the overall quality of life for older adults, reducing the incidence of isolation, anxiety, and depression, and promoting healthier, more vibrant communities.Clinical trial numberNot applicable.

BackgroundFear of falling (FoF) and depression substantially affect the health outcomes of older adults. Previous studies have established an association between FoF and depression; however, the moderating role of social capital in this relationship remains underexplored. We aimed to investigate the moderating effect of social capital on the relationship between FoF and depressive symptoms in older adults.MethodsData from 773,861 adults aged ≥ 65 years who participated in the 2019 Korean Community Health Survey (KCHS) were used for this cross-sectional study. The assessment tools included measures of FoF, the Patient Health Questionnaire-9, and indicators of social capital (neighborhood trust, neighborly reciprocity, neighborhood satisfaction, social networks, and social participation). Data were analyzed using a linear regression model with the complex survey module, which accounts for stratification, clustering, and sampling weights inherent in the KCHS design.ResultsHigher levels of FoF were significantly associated with increased depressive symptoms (B = 0.94, 95% confidence interval [CI] = 0.88, 1.00). Social capital significantly moderated this FoF–depression relationship, with the strongest effects observed for social participation (B = -0.21, 95% CI = -0.26, -0.15) and social networks (B = -0.16, 95% CI = -0.23, -0.10), followed by neighborhood satisfaction (B = -0.12, 95% CI = -0.17, -0.06). These interactions indicate that higher social capital attenuates the positive association between FoF and depressive symptoms.ConclusionsThe critical role of social capital in moderating the relationship between FoF and depressive symptoms in community-dwelling older adults was empirically validated. Using a nationally representative dataset and complex sampling design, the generalizability of the findings was enhanced. The findings underscore the need for social capital enhancement strategies, such as strengthening social networks, expanding social participation opportunities, and developing age-friendly environments, to prevent and manage FoF and depression among community-dwelling older adults.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13690-025-01714-0.

BackgroundPrevious studies have shown that diet is closely related to the subjective well-being, but the interaction between dietary diversity and subjective well-being over time has not been reported. This study is designed to provide fresh insights into the relationship between dietary diversity and subjective well-being within a cohort of older Chinese adults.MethodsData were sourced from the China Longitudinal Survey of Health and Longevity conducted in three waves: 2008–2009 (T1), 2011–2012 (T2), and 2014 (T3). Dietary diversity was determined by the frequency of consumption across various food groups. Subjective well-being was evaluated based on life satisfaction, positive emotions, and negative emotions. The random intercept cross-lagged panel model and linear regression model were employed to analyze the associations between dietary diversity and subjective well-being.ResultsThe study found that older adults with a higher dietary diversity among 3,680 older participants reported higher subjective well-being. Dietary diversity was positively correlated with subjective well-being across all three waves (rs = 0.143–0.266, ps < 0.01; βs = 0.23–0.40, ps < 0.001). The association remains significant at the between-person level (β = 0.166, p < 0.001) and within-person level (βs = 0.174–0.242, p < 0.001). The random intercept cross-lagged model also indicated an increase in T1 subjective well-being predicted an increase in T2 dietary diversity (β = 0.017, p < 0.05) and an increase in T2 dietary diversity predicted an increase in T3 subjective well-being (β = 0.039, p < 0.05). Sensitivity analyses indicated that the association between dietary diversity and subjective well-being was robust.ConclusionsA longitudinal, reciprocal relationship exists between dietary diversity and subjective well-being in Chinese older adults. The findings underscore the significance of a diversified diet in enhancing the subjective well-being of older adults.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13690-025-01721-1.