No abstract

No abstract

BackgroundRural patients experience worse cancer survival outcomes than urban patients despite similar incidence rates, due in part to significant barriers to accessing quality cancer care. Community hospitals in non-metropolitan/rural areas play a crucial role in providing care to patients who desire and are able to receive care locally. However, rural community hospitals typically face challenges to providing comprehensive care due to lack of resources. The University of Kentucky's Markey Cancer Center Affiliate Network (MCCAN) is an effective complex, multi-level intervention, improving cancer care in rural/under-resourced hospitals by supporting them in achieving American College of Surgeons Commission on Cancer (CoC) standards. With the long-term goal of adapting MCCAN for other rural contexts, we aimed to identify MCCAN's core functions (i.e., the components key to the intervention's effectiveness/implementation) using theory-driven qualitative data research methods.MethodsWe conducted eight semi-structured virtual interviews with administrators, coordinators, clinicians, and certified tumor registrars from five MCCAN affiliate hospitals that were not CoC-accredited prior to joining MCCAN. Study team members coded interview transcripts and identified themes related to how MCCAN engaged affiliate sites in improving care quality (intervention functions) and implementing CoC standards (implementation functions) and analyzed themes to identify core functions. We then mapped core functions onto existing theories of change and presented the functions to MCCAN leadership to confirm validity and completeness of the functions.ResultsIntervention core functions included: providing expertise and templates for achieving accreditation, establishing a culture of quality-improvement among affiliates, and fostering a shared goal of quality care. Implementation core functions included: fostering a sense of community and partnership, building trust between affiliates and Markey, providing information and resources to increase feasibility and acceptability of meeting CoC standards, and mentoring and empowering administrators and clinicians to champion implementation.ConclusionThe MCCAN intervention presents a more equitable strategy of extending the resources and expertise of large cancer centers to assist smaller community hospitals in achieving evidence-based standards for cancer care. Using rigorous qualitative methods, we distilled this intervention into its core functions, positioning us (and others) to adapt the MCCAN intervention to address cancer disparities in other rural contexts.

BackgroundA consistent approach to cervical spine injury (CSI) clearance for patients 65 and older remains a challenge. Clinical clearance algorithms like the National Emergency X-Radiography Utilisation Study (NEXUS) criteria have variable accuracy and the Canadian C-spine rule excludes older patients. Routine CT of the cervical spine is performed to rule out CSI but at an increased cost and low yield. Herein, we aimed to identify predictive clinical variables to selectively screen older patients for CSI.MethodsThe University of Iowa’s trauma registry was interrogated to retrospectively identify all patients 65 years and older who presented with trauma from a ground-level fall from January 2012 to July 2017. The relationship between predictive variables (demographics, NEXUS criteria and distracting injuries) and presence of CSI was examined using the generalised linear modelling (GLM) framework. A training set was used to build the statistical models to identify clinical variables that can be used to predict CSI and a validation set was used to assess the reliability and consistency of the model coefficients estimated from the training set.ResultsOverall, 2312 patients ≥65 admitted for ground-level falls were identified; 253 (10.9%) patients had a CSI. Using the GLM framework, the best predictive model for CSI included midline tenderness, focal neurological deficit and signs of trauma to the head/face, with midline tenderness highly predictive of CSI (OR=22.961 (15.178–34.737); p<0.001). The negative predictive value (NPV) for this model was 95.1% (93.9%–96.3%). In the absence of midline tenderness, the best model included focal neurological deficit (OR=2.601 (1.340–5.049); p=0.005) and signs of trauma to the head/face (OR=3.024 (1.898–4.815); p<0.001). The NPV was 94.3% (93.1%–95.5%).ConclusionMidline tenderness, focal neurological deficit and signs of trauma to the head/face were significant in this older population. The absence of all three variables indicates lower likelihood of CSI for patients≥65. Future observational studies are warranted to prospectively validate this model.

No abstract