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Objective: Norway has a high incidence of forearm fractures, however, the incidence rates based on secondary care registers can be underestimated, as some fractures are treated exclusively in primary care. We estimated the proportion of forearm fracture diagnoses registered exclusively in primary care and assessed the agreement between diagnosis for forearm fractures in primary and secondary care. Design: Quality assurance study combining nationwide data from 2008 to 2019 on forearm fractures registered in primary care (Norwegian Control and Payment of Health Reimbursement) and secondary care (the Norwegian Patient Registry). Setting and patients: Forearm fracture diagnoses in patients aged ≥20 treated in primary care (n = 83,357) were combined with injury diagnoses for in- and outpatients in secondary care (n = 3,294,336). Main outcome measures: Proportion of forearm fractures registered exclusively in primary care, and corresponding injury diagnoses for those registered in both primary and secondary care. Results: Of 189,105 forearm fracture registrations in primary and secondary care, 13,948 (7.4%) were registered exclusively in primary care. The proportion ranged from 4.9% to 13.5% on average between counties, but was higher in some municipalities (>30%). Of 66,747 primary care forearm fractures registered with a diagnosis in secondary care, 62% were incident forearm fractures, 28% follow-up controls, and 10% other fractures or non-fracture injuries. Conclusion: An overall small proportion of forearm fractures were registered only in primary care, but it was larger in some areas of Norway. Failing to include fractures exclusively treated in primary care could underestimate the incidence rates in these areas. Norwegian forearm fracture incidence based on secondary care may be underestimated by not including fractures treated exclusively in primary care. The mean proportion of forearm fractures exclusively handled in primary care is 7% and varies from 5% to 14% between counties. Fractures treated in primary care can be considered for more accurate national incidence rates. Correct fracture diagnosis needs further investigation.

Copyright © 2021 Foessl, Bassett, Bjørnerem, Busse, Calado, Chavassieux, Christou, Douni, Fiedler, Fonseca, Hassler, Högler, Kague, Karasik, Khashayar, Langdahl, Leitch, Lopes, Markozannes, McGuigan, Medina-Gomez, Ntzani, Oei, Ohlsson, Szulc, Tobias, Trajanoska, Tuzun, Valjevac, van Rietbergen, Williams, Zekic, Rivadeneira and Obermayer-Pietsch. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms., A synoptic overview of scientific methods applied in bone and associated research fields across species has yet to be published. Experts from the EU Cost Action GEMSTONE ("GEnomics of MusculoSkeletal Traits translational Network") Working Group 2 present an overview of the routine techniques as well as clinical and research approaches employed to characterize bone phenotypes in humans and selected animal models (mice and zebrafish) of health and disease. The goal is consolidation of knowledge and a map for future research. This expert paper provides a comprehensive overview of state-of-the-art technologies to investigate bone properties in humans and animals - including their strengths and weaknesses. New research methodologies are outlined and future strategies are discussed to combine phenotypic with rapidly developing -omics data in order to advance musculoskeletal research and move towards "personalised medicine"., Funding was obtained from the GEMSTONE COST Action (CA18139). The Origins of Bone and Cartilage Disease Programme analysed the skeletal phenotypes of knockout mice generated by the International Mouse Phenotyping Consortium (IMPC) and was funded by a Welcome Trust Strategic Award (101123) to GRW and JHDB. IF is enrolled in the PhD program MOLIN at the Medical University of Graz, funded by the Austrian Science Fund (FWF). FM is funded by the Swedish Research Council (2018-02981), Greta and Johan Kock Foundation, the A. Påhlsson, A. Osterlund Foundation and H Järnhardt Foundations, King Gustav V 80 year fund, Swedish Rheumatism foundation, Skåne University Hospital Research Fund, Research and Development Council of Region Skåne.

Summary Determinants of trabecular bone score (TBS) and vertebral fractures assessed semiquantitatively (SQ1–SQ3) were studied in 496 women with fragility fractures. TBS was associated with age, parental hip fracture, alcohol intake and BMD, not SQ1–SQ3 fractures. SQ1–SQ3 fractures were associated with age, prior fractures, and lumbar spine BMD, but not TBS. Introduction Trabecular bone score (TBS) and vertebral fractures assessed by semiquantitative method (SQ1–SQ3) seem to reflect different aspects of bone strength. We therefore sought to explore the determinants of and the associations between TBS and SQ1–SQ3 fractures. Methods This cross-sectional sub-study of the Norwegian Capture the Fracture Initiative included 496 women aged ≥ 50 years with fragility fractures. All responded to a questionnaire about risk factors for fracture, had bone mineral density (BMD) of femoral neck and/or lumbar spine assessed, TBS calculated, and 423 had SQ1–SQ3 fracture assessed. Results Mean (SD) age was 65.6 years (8.6), mean TBS 1.27 (0.10), and 33.3% exhibited SQ1–SQ3 fractures. In multiple variable analysis, higher age (βper SD = − 0.26, 95% CI: − 0.36,− 0.15), parental hip fracture (β = − 0.29, 95% CI: − 0.54,− 0.05), and daily alcohol intake (β = − 0.43, 95% CI − 0.79, − 0.08) were associated with lower TBS. Higher BMD of femoral neck (βper SD = 0.34, 95% CI 0.25–0.43) and lumbar spine (βper SD = 0.40, 95% CI 0.31–0.48) were associated with higher TBS. In multivariable logistic regression analyses, age (ORper SD = 1.94, 95% CI 1.51–2.46) and prior fragility fractures (OR = 1.71, 95% CI 1.09–2.71) were positively associated with SQ1–SQ3 fractures, while lumbar spine BMD (ORper SD = 0.75 95% CI 0.60–0.95) was negatively associated with SQ1–SQ3 fractures. No association between TBS and SQ1–SQ3 fractures was found. Conclusion Since TBS and SQ1–SQ3 fractures were not associated, they may act as independent risk factors, justifying the use of both in post-fracture risk assessment.

Importance Fragility fracture is a major health issue because of the accompanying morbidity, mortality, and financial cost. Despite the high cost to society and personal cost to affected individuals, secondary fracture prevention is suboptimal in Norway, mainly because most patients with osteoporotic fractures do not receive treatment with antiosteoporotic drugs after fracture repair. Objectives To improve secondary fracture prevention by introducing a standardized intervention program and to investigate the effect of the program on the rate of subsequent fractures. Design, Setting, and Participants Trial protocol of the Norwegian Capture the Fracture Initiative (NoFRACT), an ongoing, stepped wedge cluster randomized clinical trial in 7 hospitals in Norway. The participating hospitals were cluster randomized to an intervention starting date: May 1, 2015; September 1, 2015; and January 1, 2016. Follow-up is through December 31, 2019. The outcome data were merged from national registries of women and men 50 years and older with a recent fragility fracture treated at 1 of the 7 hospitals. Discussion The NoFRACT trial is intended to enroll 82 000 patients (intervention period, 26 000 patients; control period, 56 000 patients), of whom 23 578 are currently enrolled by January 2018. Interventions include a standardized program for identification, assessment, and treatment of osteoporosis in patients with a fragility fracture that is led by a trained coordinating nurse. The primary outcome is rate of subsequent fracture (per 10 000 person-years) based on national registry data. Outcomes before (2008-2015; control period) and after (2015-2019; intervention period) the intervention will be compared, and each hospital will act as its own control. Use of outcomes from national registry data means that all patients are included in the analysis regardless of whether they are exposed to the intervention (intention to treat). A sensitivity analysis with a transition window will be performed to mitigate possible within-cluster contamination. Results Results are planned to be disseminated through publications in peer-reviewed journals and presented at local, national, and international conferences. Conclusions By introducing a standardized intervention program for assessment and treatment of osteoporosis in patients with fragility fractures, we expect to document reduced rates of subsequent fractures and fracture-related mortality. This is an open access article distributed under the terms of the CC-BY License.

BACKGROUND: Absolute risk estimation is a preferred approach for assessing fracture risk and treatment decision making. This study aimed to evaluate and validate the predictive performance of the Garvan Fracture Risk Calculator in a Norwegian cohort. METHODS: The analysis included 1637 women and 1355 aged 60+ years from the Tromsø study. All incident fragility fractures between 2001 and 2009 were registered. The predicted probabilities of non-vertebral osteoporotic and hip fractures were determined using models with and without BMD. The discrimination and calibration of the models were assessed. Reclassification analysis was used to compare the models performance. RESULTS: The incidence of osteoporotic and hip fracture was 31.5 and 8.6 per 1000 population in women, respectively; in men the corresponding incidence was 12.2 and 5.1. The predicted 5-year and 10-year probability of fractures was consistently higher in the fracture group than the non-fracture group for all models. The 10-year predicted probabilities of hip fracture in those with fracture was 2.8 (women) to 3.1 times (men) higher than those without fracture. There was a close agreement between predicted and observed risk in both sexes and up to the fifth quintile. Among those in the highest quintile of risk, the models over-estimated the risk of fracture. Models with BMD performed better than models with body weight in correct classification of risk in individuals with and without fracture. The overall net decrease in reclassification of the model with weight compared to the model with BMD was 10.6% (p = 0.008) in women and 17.2% (p = 0.001) in men for osteoporotic fractures, and 13.3% (p = 0.07) in women and 17.5% (p = 0.09) in men for hip fracture. CONCLUSIONS: The Garvan Fracture Risk Calculator is valid and clinically useful in identifying individuals at high risk of fracture. The models with BMD performed better than those with body weight in fracture risk prediction.