Risultati per: Screening del cancro alla prostata

Introduction
Anxiety problems are prevalent in childhood and, without intervention, can persist into adulthood. Effective evidence-based interventions for childhood anxiety disorders exist, specifically cognitive–behavioural therapy (CBT) in a range of formats. However, only a small proportion of children successfully access and receive treatment. Conducting mental health screening in schools and integrating evidence-based interventions for childhood anxiety problems may be an effective way to ensure support reaches children in need. The Identifying Child Anxiety Through Schools—Identification to Intervention (iCATS i2i) trial involves screening for childhood anxiety problems and offering a brief online parent-led CBT intervention. This paper presents the protocol for the process evaluation of the iCATS i2i trial, which aims to examine the implementation and acceptability of the study procedures, the mechanisms of change and whether any external factors had an impact on procedure engagement or delivery.

Methods and analysis
This process evaluation will use both quantitative and qualitative methods to evaluate the implementation and acceptability of and barriers/facilitators to engagement and delivery of the iCATS screening/intervention procedures. Quantitative data sources will include opt-out and completion rates of baseline measures and usage analytics extracted from the online intervention platform. Qualitative interviews will be conducted with children, parents, school staff, iCATS i2i clinicians and researchers delivering study procedures. The Medical Research Council framework for process evaluations will guide study design and analysis.

Ethics and dissemination
This study has received ethical approval from the University of Oxford Research Ethics Committee (R66068_RE003). Findings from the study will be disseminated via peer-reviewed publications in academic journals, conferences, digital and social media platforms and stakeholder meetings.

Trial registration
ISRCTN76119074.

Si rafforza collaborazione tra Istituti, verso progetti comuni

Objective
The aim of this study was to estimate the total national direct cost of breast cancer screening from 2019 to 2022 and project the total national cost and average lifetime cost of screening per woman for three current guidelines.

Design
We estimated the national cost of screening from 2019 to 2022, and per cancer detected in 2022, using real-world data on the number of mammograms performed per year. We also projected the national cost of screening using life table modelling for three guidelines: 2021/2023 American College of Radiology (ACR), 2023 American Cancer Society (ACS) and 2024 United States Preventative Services Task Force (USPSTF). The average lifetime cost to screen one woman until age 74 years with each guideline was also estimated. The Optum Labs Data Warehouse was used to estimate commercial and Medicare costs and recall rates. Sensitivity analyses were used to estimate uncertainty and determine which inputs had the largest impact on total national costs.

Setting
This study was conducted for the USA.

Participants
Women eligible for breast cancer screening.

Interventions
Digital mammograms (2D) or digital breast tomosynthesis (3D) and/or MRI.

Primary outcome measure
Total national cost of screening calculated as the sum of screening and recall costs. Average lifetime cost of screening per woman until 74 years.

Results
Nationally, screening cost approximately US$11 billion (B) per year from 2019 to 2022 with approximately 37% of eligible women screened each year. In 2022, screening cost US$55 471 per 3D-detected and US$44 000 per 2D-detected invasive or ductal carcinoma in situ case. Using target yearly participation rates of 54%–78% by age of women, the projected cost of screening was US$30B for ACR, US$18B for ACS and US$8B for USPSTF guidelines. The average lifetime cost to screen an average-risk woman was: US$13 416 for ACR, US$7946 for ACS and US$6931 for USPSTF. Participation rates, the proportion of women with a lifetime risk >20% and commercial MRI and 3D costs had the largest impact on total costs.

Conclusion
The cost of screening varies significantly by guideline (US$8B–US$30B) and was most influenced by participation rates, high-risk population proportions and technology costs. Future work can investigate whether risk-based screening strategies being tested in ongoing clinical trials can reduce national screening costs while improving outcomes.Cite Now

Circulation, Volume 151, Issue 7, Page 508-510, February 18, 2025.

Nuovi dati dallo studio di fase III Aranote

‘Al lavoro per avviare quello del cancro al polmone’

‘Al lavoro per avviare quello del cancro al polmone’

At first glance, the updated US Preventive Services Task Force (USPSTF) Recommendation Statement on osteoporosis screening appears nearly identical to the previous 2018 statement, especially regarding the recommendation for universal screening in women 65 years or older and insufficient evidence to support a recommendation for or against screening in men. However, subtle revisions to the 2018 recommendation may result in substantive changes in screening of younger postmenopausal women in clinical practice. While a B recommendation for higher-risk postmenopausal women younger than 65 years is common to both statements, the 2018 statement recommended assessing risk of osteoporosis in these women using a formal clinical risk assessment tool, whereas the 2024 Recommendation Statement recommends screening those at increased risk for an osteoporotic fracture as estimated by clinical risk assessment. Additionally, the screening test for both younger and older postmenopausal women in the 2018 recommendation is specified broadly as bone measurement testing. By contrast, the 2024 statement is more specific and defines screening as central (hip or lumbar spine) dual-energy x-ray absorptiometry (DXA) bone mineral density (BMD) testing with or without fracture risk assessment.

This systematic review to support a 2025 US Preventive Services Task Force Recommendation Statement summarizes published evidence on the benefits and harms of screening for, and treatment of, osteoporosis to prevent fractures in adults.

This JAMA Patient Page describes osteoporosis and the US Preventive Services Task Force recommendations for screening for osteoporosis.

This 2025 Recommendation Statement from the US Preventive Services Task Force recommends screening for osteoporosis to prevent osteoporotic fractures in women 65 years or older (B recommendation) and postmenopausal women younger than 65 years at increased risk for an osteoporotic fracture (B recommendation) and concludes that current evidence is insufficient to assess the balance of benefits and harms of screening for osteoporosis to prevent osteoporotic fractures in men (I statement).

L’organizzazione accoglie a Roma chi viene da fuori per le cure

Objectives
Alzheimer’s disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Although existing AD risk prediction tools demonstrate high accuracy, their complexity and limited accessibility restrict practical application. This study aimed to develop a convenience, efficient prediction model for AD risk using machine learning techniques.

Design and setting
We conducted a cross-sectional study with participants aged 60 and older from the National Alzheimer’s Coordinating Center. We selected personal characteristics, clinical data and psychosocial factors as baseline predictors for AD (March 2015 to December 2021). The study utilised Random Forest and Extreme Gradient Boosting (XGBoost) algorithms alongside traditional logistic regression for modelling. An oversampling method was applied to balance the data set.

Interventions
This study has no interventions.

Participants
The study included 2379 participants, of whom 507 were diagnosed with AD.

Primary and secondary outcome measures
Including accuracy, precision, recall, F1 score, etc.

Results
11 variables were critical in the training phase, including educational level, depression, insomnia, age, Body Mass Index (BMI), medication count, gender, stenting, systolic blood pressure (sbp), neurosis and rapid eye movement. The XGBoost model exhibited superior performance compared with other models, achieving area under the curve of 0.915, sensitivity of 76.2% and specificity of 92.9%. The most influential predictors were educational level, total medication count, age, sbp and BMI.

Conclusions
The proposed classifier can help guide preclinical screening of AD in the elderly population.