Retrospective US database analysis of drug utilization patterns, health care resource use, and costs associated with adjuvant interferon alfa-2b therapy for treatment of malignant melanoma following surgery
Hackshaw M.D.,Sharpe and Dohme Corporation |
Krishna A.,Sharpe and Dohme Corporation |
Mauro D.J.,Merck And Co.
ClinicoEconomics and Outcomes Research | Year: 2012
Background: The purpose of this study was to identify a real-world US population having undergone surgery for malignant melanoma and describe treatment patterns, health care resource utilization, and costs for patients who subsequently received interferon alfa-2b (IFN) therapy or other standard of care chemotherapies. Methods: A retrospective cohort study was conducted using administrative claims from MarketScan® databases among melanoma patients diagnosed between 2004 and 2008 who had surgery and were subsequently treated with IFN or other chemotherapies. Health care resource utilization and costs of services (converted to 2009 dollars) were evaluated. Cost refers to the amount paid to providers associated with the health service. Results: Of 18,075 subjects with melanoma surgery claims, 1525 (8.4%) were treated with IFN and 1194 (6.6%) with other chemotherapies. Median duration (days) and number of doses of IFN therapy were 29 and 20, respectively. Approximately half of patients who received IFN discontinued therapy within or after the one-month induction phase. For IFN therapy patients, average total cost per patient for the last melanoma-related surgery prior to start of therapy, including costs of the surgery itself, pathology, anesthesia, and hospital care, was $2219. The average total cost per patient related to IFN therapy was $1188; this included costs for drug, office visits, blood work, and infusions. Other chemotherapy costs ranged from $146 to $2678. Conclusion: There is an unmet treatment need, considering that this study observed that melanoma patients on IFN therapy post-surgery do not complete the recommended one-year course of treatment which may compromise its full therapeutic benefits. Further study to investigate reasons for discontinuation may be warranted. In addition, costs associated with adjuvant IFN therapy in post-surgical treatment of disease are likely acceptable. © 2012 Hackshaw et al, publisher and licensee Dove Medical Press Ltd.
Walter L.,New York Hospital Queens |
Cabrerra L.,Asociacion Benefica PRISMA |
Gravitt P.E.,Sharpe and Dohme Corporation |
Marks M.A.,Sharpe and Dohme Corporation
Sexually Transmitted Infections | Year: 2016
Purpose The incidence of human papillomavirus (HPV) associated head and neck cancers (HNCs) have been increasing in Peru. However, the burden of oral HPV infection in Peru has not been assessed. The objective of this cross-sectional study was to estimate the prevalence and correlates of oral HPV infection in a populationbased sample from males and females from Lima, Peru. Methods Between January 2010 and June 2011, a population-based sample of 1099 individuals between the ages of 10 and 85 from a low-income neighbourhood in Lima, Peru was identified through random household sampling. Information on demographic, sexual behaviours, reproductive factors and oral hygiene were collected using intervieweradministered questionnaires. Oral rinse specimens were collected from each participant, and these specimens were genotyped using the Roche Linear Array assay. ORs were used to assess differences in the prevalence of any oral HPV and any high-risk oral HPV infection by demographic factors, sexual practices and oral hygiene among individuals 15+ years of age. Results The prevalence of any HPV and any high-risk HPV (HR-HPV) was 6.8% and 2.0%, respectively. The three most common types were HPV 55 (3.4%), HPV 6 (1.5%) and HPV 16 (1.1%). Male sex (aOR, 2.21; 95% CI 1.22 to 4.03) was associated with any HPV infection after adjustment. Conclusions The prevalence of oral HPV in this study was similar to estimates observed in the USA. Higher prevalence of oral infections in males was consistent with a male predominance of HPV-associated HNCs and may signal a sex-specific aetiology in the natural history of infection.
Lucas J.E.,Duke University |
Thompson J.W.,Duke University |
Dubois L.G.,Duke University |
McCarthy J.,Duke University |
And 10 more authors.
BMC Bioinformatics | Year: 2012
Background: Label-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.Results: We describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.Conclusions: Mass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays. © 2012 Lucas et al.; licensee BioMed Central Ltd.