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Background: Unwarranted variation in healthcare utilization can only partly be explained by variation in the health care needs of the population, yet it is frequently found globally. This is the first cross-sectional study that systematically assessed geographic variation in the adherence to clinical recommendations in Switzerland. Specifically, we explored 1) the geographic variation of adherence to clinical recommendations across 24 health services at the sub-cantonal level, 2) assessed and mapped statistically significant spatial clusters, and 3) explored possible influencing factors for the observed geographic variation.
Methods: Exploratory spatial analysis using the Moran's I statistic on multivariable multilevel model residuals to systematically identify small area variation of adherence to clinical recommendations across 24 health services.
Results: Although there was no overall spatial pattern in adherence to clinical recommendations across all health care services, we identified health services that exhibited statistically significant spatial dependence in adherence. For these, we provided evidence about the locations of local clusters.
Interpretation: We identified regions in Switzerland in which specific recommended or discouraged health care services are utilized less or more than elsewhere. Future studies are needed to investigate the place-based social determinants of health responsible for the sub-cantonal variation in adherence to clinical recommendations in Switzerland and elsewhere over time.
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http://dx.doi.org/10.1177/23333928221097741 | DOI Listing |
J Appl Clin Med Phys
September 2025
Icon Cancer Centre Toowoomba, Toowoomba, Queensland, Australia.
Introduction: The role of imaging in radiotherapy is becoming increasingly important. Verification of imaging parameters prior to treatment planning is essential for safe and effective clinical practice.
Methods: This study described the development and clinical implementation of ImageCompliance, an automated, GUI-based script designed to verify and enforce correct CT and MRI parameters during radiotherapy planning.
Basic Clin Androl
September 2025
Department of Urology, University Hospital Southampton, Southampton, UK.
Background: To compare surgical and long-term patient-reported outcomes (PRO) between excisional (Nesbit) and incisional (Yachia) corporoplasty for correction of uncomplicated Peyronie's-related penile curvature in a large, single-surgeon cohort. A retrospective audit identified men who underwent Nesbit or Yachia corporoplasty (2015-2021). Operative data was extracted from records.
View Article and Find Full Text PDFBMC Cardiovasc Disord
September 2025
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Sociology and Rehabilitation Science, Charitéplatz 1, 10117, Berlin, Germany.
Background: Myocardial infarctions (MI) significantly contribute to the global disease burden and are often followed by psychological conditions such as depression, anxiety, and posttraumatic stress disorder (PTSD). These are frequently underrecognized and insufficiently addressed in clinical care. This study aims to investigate the psychosocial impact of MI, identify risk factors for psychological burden following an MI, and gain insight into the perceived psychological care during hospitalization.
View Article and Find Full Text PDFBMC Pediatr
September 2025
School of Health and Welfare, Halmstad University, Halmstad, Sweden.
Background: Adequate sleep is crucial for children's health, especially for children with ADHD and concurrent sleep problems. There is a need for more studies focusing on sleep problems in children with ADHD as these problems may exacerbate ADHD symptoms and vice versa, impacting negatively on everyday life. The aim of this study was to investigate the differences in health-related factors between children with ADHD without clinically relevant sleep problems and those with clinically relevant sleep problems after a sleep intervention.
View Article and Find Full Text PDFBMC Musculoskelet Disord
September 2025
Department of Clinical Sciences at Danderyds Hospital, Department of Orthopedic Surgery, Karolinska Institutet, Stockholm, 182 88, Sweden.
Background: This study evaluates the accuracy of an Artificial Intelligence (AI) system, specifically a convolutional neural network (CNN), in classifying elbow fractures using the detailed 2018 AO/OTA fracture classification system.
Methods: A retrospective analysis of 5,367 radiograph exams visualizing the elbow from adult patients (2002-2016) was conducted using a deep neural network. Radiographs were manually categorized according to the 2018 AO/OTA system by orthopedic surgeons.