scholarly journals Measures of economic advantage associated with HPV-positive head and neck cancers among non-Hispanic black and white males identified through the National Cancer Database

2017 ◽  
Vol 48 ◽  
pp. 1-7 ◽  
Author(s):  
Caryn E. Peterson ◽  
Shaveta Khosla ◽  
Gina D. Jefferson ◽  
Faith G. Davis ◽  
Marian L. Fitzgibbon ◽  
...  
2018 ◽  
Vol 36 (7_suppl) ◽  
pp. 26-26
Author(s):  
Katherine Ramsey Gilmore ◽  
Guadalupe R. Palos ◽  
Patricia Chapman ◽  
Paula A. Lewis-Patterson ◽  
Weiqi Bi ◽  
...  

26 Background: Research indicates survivors of head and neck cancers (HNC) are at greater risk for recurrences and adverse late effects. HNC survivorship algorithms are available to provide consistent and optimal care to this at-risk group. Here, we describe practitioners’ concordance with algorithms aimed at survivors diagnosed with cancers of the oral cavity (OC) oropharynx (OP), and larynx/hypopharynx (LH). Methods: We reviewed medical records, treatment summaries, and survivorship care plans of patients seen in the HNC Survivorship Clinic from 09/01/2011 to 08/31/2014. The primary outcome was providers’ rate of concordance (> 90%) with 3 HNC algorithms. Concordance rates (CRs) were measured and compared within and between groups. Results: 145 patients met the eligibility criteria. Demographics varied by cancer sub-type: 19.3% OC, 58.6% OP, and 22.1% LH. Most were white males with the mean age of 62.7 years at first visit during the study period. Table 1 indicates clinicians’ CRs ranged from 9.2% to 99.7%. Overall, CRs were highly consistent and met or exceeded the benchmark for H & P, chest x-ray, and dysphagia and lymphedema assessment. Body image assessment had the lowest CRS. Wide variation in CRs existed within and between sites for: T4/TSH, head/neck CTs, xerostomia assessments. Conclusions: Our findings indicate providers’ practice did deviate from recommendations listed in the HNC algorithms. Increasing clinicians' awareness and education may help achieve optimal adherence to survivorship algorithms. [Table: see text]


2017 ◽  
Vol 16 (4) ◽  
pp. 56-61
Author(s):  
N.S. Grachev ◽  
◽  
I.N. Vorozhtsov ◽  
N.V. Babaskina ◽  
E.Yu. Iaremenko ◽  
...  

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