Maria Teresa Riccardi

Background/Objectives: Colorectal (CRC), breast (BC), and cervical cancer (CC) pose a significant health burden, yet screening programs have been proven to reduce cancer-specific mortality and other non-lethal endpoints. Mobile health (mHealth) technologies can enhance adherence, but effectiveness varies. This scoping review aims to explore mHealth apps for cancer screening developed with community engagement, identifying research approaches and gaps. Methods: A scoping review following PRISMA-ScR guidelines analyzed studies on mHealth apps for cancer screening developed through community engagement. Community engagement was classified per WHO’s definition. Databases were searched using a PCC-based strategy; eligible studies involved app development, excluding hypothetical apps or text messaging-/social media-only interventions. Screening and data extraction were conducted independently. Results: Thirteen articles were included. Findings indicate a growing but limited body of evidence, with most studies focusing on CRC and BC and involving minority populations through mHealth apps. Key engagement phases included research design, CAB establishment, and recruitment, while priority setting was never community-led. The wMammogram, Meet ALEX, and mMammogram apps improved screening knowledge, intention, and participation, while ColorApp enhanced knowledge but not attitudes. Only CBPR-based studies included dissemination, and one involved the CAB in data analysis. Some studies acknowledged community contributions, though details on ColorApp’s engagement were limited. Conclusions: Standardized engagement frameworks combined with mHealth were associated with greater community involvement and may improve equity. No community-designed mHealth app was found for CC screening, despite its relevance. Future research should address gaps in CC programs, prioritize early community involvement, and assess the long-term impact of mHealth interventions.

: In Italy,the National Breast Screening Programme offers mammography as the 1st level examination, blindly assessed by two radiologists.In the context of continuous quality control,discrepancies in the assessment of mammograms between the 1st and 2nd reader have been studied,focusing on the most significant ones.The aims of the present study are:to evaluate the outcome of women sent to the 2nd level of review;to evaluate the possibility of using artificial intelligence (AI) software to assist radiologists in case of discrepancies. In the Local Health Authority Roma2,radiologists use a standardised classification that rates radiographs on a five-level scale from R1 to R5:R1=no abnormalities,R2=benign findings,R3=equivocal findings,R4=suspected cancer,R5=strongly suspected cancer.Data were extracted from the mammograms performed in 2023 that showed discrepancies (R1/R2 vs. R4/R5).2nd level mammography≥R4 and 2nd level histology≥B4 were considered positive. For the second aim,”IA Lunit INSIGHT” software was used to create specific target groups using the following cut-offs:G1<10%, G2 11-29%; G3 30-59%; G4 60-85%; G5 86-100%(groups ≥G3 were considered positive). Of the 36,339 mammograms,524(1.5%) were discordant:347(66.2%) were considered suspicious by the second radiologist.49(9.4%) were confirmed positive by Level II mammography and 23(4.4%) reported histology≥B4. The AI red a random sample of 247 mammograms and classified 34 women as positive (1 G5, 6 G4, 9 G3), of whom 11 (4.5%) were confirmed positive by Level II mammography and 6 (2.4%) were histologically positive. Of the negative women, 2 had histology≥B4. Discrepancies may be one of the factors leading to inappropriate referral to second level investigation. In such cases, the use of AI may improve the appropriateness of further investigation, although this hypothesis needs to be tested in larger numbers, with additional comparisons with experienced radiologists and consideration of retraining. Key messages: • Continuous monitoring in cancer screening programs are vital to assure a sustainability of the process. • Artificial Intelligence software are promising in aiding health care professionals in case of discrepancies.

As more people live in cities, the impact of urban settings on population health has been increasing. One of the main strategies to cope with urbanization is adopting artificial intelligence (AI) and new digital technologies to develop new urban services that improve citizens’ health and well-being. The aim of this study is to review urban interventions and adopting digital technologies and AI-based algorithms to improve population health. A scoping review of the literature was conducted by querying MEDLINE, Web of Science, and Scopus databases. The included studies were categorized into one urban health area, suggested by the WHO, according to the type of intervention investigated. Out of 3733 records screened, 12 papers met all inclusion criteria. Four studies investigated the “outdoor and indoor pollution” area, one “climate change”, one “housing”, two “health and social services” and four “urban transport” areas. Only one article used a comprehensive approach to public health, investigating the use of AI and digital technologies both to characterize exposure conditions to health determinants and to monitor population health effects, while the others were limited to characterizing exposure conditions to health determinants, thus employing a preliminary public health perspective. From this point of view, countries should foster synergy for the development of research on digital technologies to address the determinants of health in the urban context. From a global health perspective, sharing results with the scientific community would also allow other countries to use those technologies that have been shown to be effective, paving the way for more sustainable living conditions worldwide.

The present review aims to map the current literature on educational interventions to promote food literacy in type 2 diabetes, with a particular focus on the concept of patient engagement. The systematic review was implemented on five databases with no restrictions on the publication year. The studies selected for the review were focused on patients with type 2 diabetes, ranging from 2003 to 2021 and published in 13 countries (44% USA). Thirty-three articles were analyzed. Twenty-seven articles targeted singular patients; fifteen articles conceptualized patient engagement as self-management. In seven articles, the provider is a multidisciplinary team. Twenty articles did not report a theoretical framework in the intervention development, and eleven did not use an intervention material. Twenty-six articles did not use a technology proxy. Outcome categories were narratively mapped into four areas: clinical, psychological, behavioral, and literacy. To date, most of the interventions are heterogeneous in the adopted methodology, measures, and outcomes considered. More attention should be given to the psychosocial characterization of patient engagement as well as the technological support. High-quality, randomized controlled trials and longitudinal studies are lacking and need to be conducted to verify the efficacy of these insights.

As the COVID-19 outbreak traveled through various Italian regions, all national and local administrations issued measures to counter the spread of the contagion and organize healthcare. The Italian healthcare system is, indeed, a decentralized system with 21 regional health systems (RHSs), with different models of healthcare service delivery and organization. This study investigates whether a different organization of RHSs would have led to different management of the COVID-19 epidemic, and evaluates the effect of different approaches in epidemic management on the COVID-19 epidemiological trend. A set of indicators is identified by conducting an online synchronous Focus Group, involving an experts panel. A Pearson’s correlation test was performed on the values assumed by the historical series of indicators investigate correlations among the trends represented by the indicators or between them and external factors. The comparison between the experiences of the different Italian regions, regarding the management of the epidemic, has helped to confirm and emphasize the importance of a community-based approach in health care—integrated with the hospital’s functions for the care of complex conditions and the need for specialized assistance.

Worldwide, chronic diseases are burdening and the health systems need to be rethought to better manage this epidemiologic shift. One of the critical points in the care pathway of chronic patients is the transition from one care setting to another. Aim of this study is to provide an overview of the current evidence on the impact of transitional care programs on health and economic outcomes for chronic patients Medline, Web of Science and EMBASE were queried for relevant reviews using the Population-Intervention-Context-Outcome (PICO) model. The quality of the included articles was determined using A MeaSurement Tool to Assess systematic Reviews (AMSTAR 2). Data were analyzed using descriptive statistic, and comparison among studies carried out in European Union (EU) versus non-EU was performed (Chi-square test was used and a p < 0.05 was deemed as statistically significant) 124 reviews were assessed for eligibility and 14 were eventually included (for a total of 167 primary articles). Quality appraisal was critically low in 60% of the reviews. Both hospital readmission rate and Emergency Department (ED) visit rate were lower than those in usual care group, but this difference was significant in 40% of articles. In EU studies readmission rate was lower in 65% of cases while in non-EU ones the percentage was 51.0%, but the difference was not significant (p = 0.23). Six reviews (43%) investigated the economic impact of the transitional care: most reported an initial increase in cost due to investment in staff training and creation of organizational networks, followed by a sharp decrease in costs due to a better utilization of health services, thus leading to a reduction in overall costs. Compared with usual care, transitional care shows an overall cost reduction, even if with limited effects on re-hospitalization or ED visit rates. These findings should encourage decision makers to invest in the development of this kind of programs in order to identify models that best perform. Key messages: The patient transfer supervision from one care setting to another is necessary for continuity of care, but there is no robust evidence about the better performance of transitional care models. Systematically reviewed transitional care models has been shown be more cost saving, with a moderate impact on hospital readmission or emergency department visits rates.